[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-jenetics--jenetics":3,"tool-jenetics--jenetics":61},[4,18,26,36,44,53],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":10,"last_commit_at":24,"category_tags":25,"status":17},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,"2026-04-05T11:01:52",[14,15,13],{"id":27,"name":28,"github_repo":29,"description_zh":30,"stars":31,"difficulty_score":32,"last_commit_at":33,"category_tags":34,"status":17},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",159636,2,"2026-04-17T23:33:34",[14,13,35],"语言模型",{"id":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":32,"last_commit_at":42,"category_tags":43,"status":17},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",108322,"2026-04-10T11:39:34",[14,15,13],{"id":45,"name":46,"github_repo":47,"description_zh":48,"stars":49,"difficulty_score":32,"last_commit_at":50,"category_tags":51,"status":17},6121,"gemini-cli","google-gemini\u002Fgemini-cli","gemini-cli 是一款由谷歌推出的开源 AI 命令行工具，它将强大的 Gemini 大模型能力直接集成到用户的终端环境中。对于习惯在命令行工作的开发者而言，它提供了一条从输入提示词到获取模型响应的最短路径，无需切换窗口即可享受智能辅助。\n\n这款工具主要解决了开发过程中频繁上下文切换的痛点，让用户能在熟悉的终端界面内直接完成代码理解、生成、调试以及自动化运维任务。无论是查询大型代码库、根据草图生成应用，还是执行复杂的 Git 操作，gemini-cli 都能通过自然语言指令高效处理。\n\n它特别适合广大软件工程师、DevOps 人员及技术研究人员使用。其核心亮点包括支持高达 100 万 token 的超长上下文窗口，具备出色的逻辑推理能力；内置 Google 搜索、文件操作及 Shell 命令执行等实用工具；更独特的是，它支持 MCP（模型上下文协议），允许用户灵活扩展自定义集成，连接如图像生成等外部能力。此外，个人谷歌账号即可享受免费的额度支持，且项目基于 Apache 2.0 协议完全开源，是提升终端工作效率的理想助手。",100752,"2026-04-10T01:20:03",[52,13,15,14],"插件",{"id":54,"name":55,"github_repo":56,"description_zh":57,"stars":58,"difficulty_score":32,"last_commit_at":59,"category_tags":60,"status":17},4721,"markitdown","microsoft\u002Fmarkitdown","MarkItDown 是一款由微软 AutoGen 团队打造的轻量级 Python 工具，专为将各类文件高效转换为 Markdown 格式而设计。它支持 PDF、Word、Excel、PPT、图片（含 OCR）、音频（含语音转录）、HTML 乃至 YouTube 链接等多种格式的解析，能够精准提取文档中的标题、列表、表格和链接等关键结构信息。\n\n在人工智能应用日益普及的今天，大语言模型（LLM）虽擅长处理文本，却难以直接读取复杂的二进制办公文档。MarkItDown 恰好解决了这一痛点，它将非结构化或半结构化的文件转化为模型“原生理解”且 Token 效率极高的 Markdown 格式，成为连接本地文件与 AI 分析 pipeline 的理想桥梁。此外，它还提供了 MCP（模型上下文协议）服务器，可无缝集成到 Claude Desktop 等 LLM 应用中。\n\n这款工具特别适合开发者、数据科学家及 AI 研究人员使用，尤其是那些需要构建文档检索增强生成（RAG）系统、进行批量文本分析或希望让 AI 助手直接“阅读”本地文件的用户。虽然生成的内容也具备一定可读性，但其核心优势在于为机器",93400,"2026-04-06T19:52:38",[52,14],{"id":62,"github_repo":63,"name":64,"description_en":65,"description_zh":66,"ai_summary_zh":66,"readme_en":67,"readme_zh":68,"quickstart_zh":69,"use_case_zh":70,"hero_image_url":71,"owner_login":64,"owner_name":72,"owner_avatar_url":73,"owner_bio":74,"owner_company":75,"owner_location":76,"owner_email":77,"owner_twitter":78,"owner_website":79,"owner_url":80,"languages":81,"stars":119,"forks":120,"last_commit_at":121,"license":122,"difficulty_score":123,"env_os":124,"env_gpu":125,"env_ram":125,"env_deps":126,"category_tags":132,"github_topics":133,"view_count":32,"oss_zip_url":75,"oss_zip_packed_at":75,"status":17,"created_at":147,"updated_at":148,"faqs":149,"releases":178},9018,"jenetics\u002Fjenetics","jenetics","Jenetics - Genetic Algorithm, Genetic Programming, Grammatical Evolution, Evolutionary Algorithm, and Multi-objective Optimization","Jenetics 是一款基于现代 Java 编写的进化计算库，专注于提供遗传算法、遗传规划、语法演化及多目标优化等核心功能。它旨在帮助开发者高效解决复杂的优化难题，无论是寻找函数的最小值还是最大值，都能在不需手动调整底层算法细节的情况下自动完成。\n\n这款工具特别适合软件工程师、数据科学家及学术研究人员使用，尤其是那些需要在 Java 生态中构建智能优化系统或进行算法实验的群体。Jenetics 的独特之处在于其清晰的架构设计，将基因、染色体、种群等概念进行了明确分离，使得代码逻辑直观易懂。更值得一提的是，它创新性地引入了“进化流”（EvolutionStream）机制，完美契合 Java Stream API 的操作习惯。这意味着用户可以像处理普通数据流一样，通过简洁的链式调用来执行复杂的进化步骤，极大地提升了开发效率与代码的可读性。此外，Jenetics 还提供了丰富的扩展模块，支持非标准遗传操作及多目标问题求解，并配有详尽的文档与用户手册，是 Java 领域进行进化算法开发的强力助手。","# Jenetics\n\n[![Build Status](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Factions\u002Fworkflows\u002Fgradle.yml\u002Fbadge.svg)](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Factions?query=branch%3Amaster)\n[![Maven Central Version](https:\u002F\u002Fimg.shields.io\u002Fmaven-central\u002Fv\u002Fio.jenetics\u002Fjenetics?color=green)](https:\u002F\u002Fcentral.sonatype.com\u002Fartifact\u002Fio.jenetics\u002Fjenetics)\n[![Javadoc](https:\u002F\u002Fwww.javadoc.io\u002Fbadge\u002Fio.jenetics\u002Fjenetics.svg)](http:\u002F\u002Fwww.javadoc.io\u002Fdoc\u002Fio.jenetics\u002Fjenetics)\n\n**Jenetics** is a **Genetic Algorithm**, **Evolutionary Algorithm**, **Grammatical Evolution**, **Genetic Programming**, and **Multi-objective Optimization** library, written in modern day Java. It is designed with a clear separation of the several concepts of the algorithm, e.g. `Gene`, `Chromosome`, `Genotype`, `Phenotype`, `Population` and fitness `Function`. **Jenetics** allows you to minimize and maximize the given fitness function without tweaking it. In contrast to other GA implementations, the library uses the concept of an evolution stream (`EvolutionStream`) for executing the evolution steps. Since the `EvolutionStream` implements the Java Stream interface, it works smoothly with the rest of the Java Stream API.\n\n**Other languages**\n\n* [**Jenetics.Net**](https:\u002F\u002Fgithub.com\u002Frmeindl\u002Fjenetics.net): Experimental .NET Core port in C# of the base library. \n* [**Helisa**](https:\u002F\u002Fgithub.com\u002Fsoftwaremill\u002Fhelisa\u002F): Scala wrapper around the Jenetics library.\n\n## Star History\n\n[![Star History Chart](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjenetics_jenetics_readme_91d1ee7c84d1.png)](https:\u002F\u002Fwww.star-history.com\u002F#jenetics\u002Fjenetics&Date)\n\n## Documentation\n\nThe library is fully documented ([javadoc](https:\u002F\u002Fjenetics.io\u002Fjavadoc\u002Fcombined\u002F9.0\u002Findex.html)) and comes with a user manual ([pdf](http:\u002F\u002Fjenetics.io\u002Fmanual\u002Fmanual-9.0.0.pdf)).\n\n## Build Jenetics\n\n**Jenetics** requires at least **Java 25** to compile and run.\n\nCheck out the master branch from GitHub.\n\n    $ git clone https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics.git \u003Cbuilddir>\n\nJenetics uses [Gradle](http:\u002F\u002Fwww.gradle.org\u002Fdownloads) as a build system and organizes the source into *sub*-projects (modules). Each subproject is located in its own subdirectory:\n\n**Published projects**\n\nThe following projects\u002Fmodules are also published to Maven.\n\n* **[jenetics](jenetics)** [![Javadoc](https:\u002F\u002Fwww.javadoc.io\u002Fbadge\u002Fio.jenetics\u002Fjenetics.svg)](http:\u002F\u002Fwww.javadoc.io\u002Fdoc\u002Fio.jenetics\u002Fjenetics): This project contains the source code and tests for the Jenetics core-module.\n* **[jenetics.ext](jenetics.ext)** [![Javadoc](https:\u002F\u002Fwww.javadoc.io\u002Fbadge\u002Fio.jenetics\u002Fjenetics.svg)](http:\u002F\u002Fwww.javadoc.io\u002Fdoc\u002Fio.jenetics\u002Fjenetics.ext): This module contains additional _non_-standard GA operations and data types. It also contains classes for solving multi-objective problems (MOEA) and doing Grammatical Evolution (GE). \n* **[jenetics.prog](jenetics.prog)** [![Javadoc](https:\u002F\u002Fwww.javadoc.io\u002Fbadge\u002Fio.jenetics\u002Fjenetics.svg)](http:\u002F\u002Fwww.javadoc.io\u002Fdoc\u002Fio.jenetics\u002Fjenetics.prog): The modules contain classes that allow to do genetic programming (GP). It seamlessly works with the existing `EvolutionStream` and evolution `Engine`.\n* **[jenetics.xml](jenetics.xml)** [![Javadoc](https:\u002F\u002Fwww.javadoc.io\u002Fbadge\u002Fio.jenetics\u002Fjenetics.svg)](http:\u002F\u002Fwww.javadoc.io\u002Fdoc\u002Fio.jenetics\u002Fjenetics.xml): XML marshaling module for the _Jenetics_ base data structures.\n\n**Non-published modules**\n\n* **[jenetics.distassert](jenetics.distassert)**: This module allows testing whether some sample data follows a given statistical distribution. Jenetics uses this module for testing its GA operators.\n* **[jenetics.example](jenetics.example)**: This module contains example code for the *core*-module.\n* **[jenetics.doc](jenetics.doc)**: Contains the code of the website and the manual.\n* **[jenetics.tool](jenetics.tool)**: This module contains classes used for doing integration testing and algorithmic performance testing. It is also used for creating GA performance measures and creating diagrams from the performance measures.\n\nFor building the library change into the `\u003Cbuilddir>` directory (or one of the module directories) and call one of the available tasks:\n\n* **compileJava**: Compiles the Jenetics sources and copies the class files to the `\u003Cbuilddir>\u002F\u003Cmodule-dir>\u002Fbuild\u002Fclasses\u002Fmain` directory.\n* **jar**: Compiles the sources and creates the JAR files. The artifacts are copied to the `\u003Cbuilddir>\u002F\u003Cmodule-dir>\u002Fbuild\u002Flibs` directory.\n* **javadoc**: Generates the API documentation. The Javadoc is stored in the `\u003Cbuilddir>\u002F\u003Cmodule-dir>\u002Fbuild\u002Fdocs` directory\n* **test**: Compiles and executes the unit tests. The test results are printed onto the console, and a test-report, created by TestNG, is written to `\u003Cbuilddir>\u002F\u003Cmodule-dir>` directory.\n* **clean**: Deletes the `\u003Cbuilddir>\u002Fbuild\u002F*` directories and removes all generated artifacts.\n\nFor building the library jar from the source call\n\n    $ cd \u003Cbuild-dir>\n    $ .\u002Fgradlew jar\n\n\n## Example\n\n### Hello World (Ones counting)\n\nThe minimum evolution Engine setup needs a genotype factory, `Factory\u003CGenotype\u003C?>>`, and a fitness `Function`. The `Genotype` implements the `Factory` interface and can therefore be used as prototype for creating the initial `Population` and for creating new random `Genotypes`.\n\n```java\nimport io.jenetics.BitChromosome;\nimport io.jenetics.BitGene;\nimport io.jenetics.Genotype;\nimport io.jenetics.engine.Engine;\nimport io.jenetics.engine.EvolutionResult;\nimport io.jenetics.util.Factory;\n\npublic class HelloWorld {\n    \u002F\u002F 2.) Definition of the fitness function.\n    private static Integer eval(Genotype\u003CBitGene> gt) {\n        return gt.chromosome()\n            .as(BitChromosome.class)\n            .bitCount();\n    }\n\n    public static void main(String[] args) {\n        \u002F\u002F 1.) Define the genotype (factory) suitable\n        \u002F\u002F     for the problem.\n        Factory\u003CGenotype\u003CBitGene>> gtf =\n            Genotype.of(BitChromosome.of(10, 0.5));\n\n        \u002F\u002F 3.) Create the execution environment.\n        Engine\u003CBitGene, Integer> engine = Engine\n            .builder(HelloWorld::eval, gtf)\n            .build();\n\n        \u002F\u002F 4.) Start the execution (evolution) and\n        \u002F\u002F     collect the result.\n        Genotype\u003CBitGene> result = engine.stream()\n            .limit(100)\n            .collect(EvolutionResult.toBestGenotype());\n\n        System.out.println(\"Hello World:\\n\" + result);\n    }\n}\n```\n\nIn contrast to other GA implementations, the library uses the concept of an evolution stream (`EvolutionStream`) for executing the evolution steps. Since the `EvolutionStream` implements the Java Stream interface, it works smoothly with the rest of the Java streaming API. Now let's have a closer look at the listing above and discuss this simple program step by step:\n\n1. The probably most challenging part, when setting up a new evolution `Engine`, is to transform the problem domain into a appropriate `Genotype` (factory) representation. In our example we want to count the number of ones of a `BitChromosome`. Since we are counting only the ones of one chromosome, we are adding only one `BitChromosome` to our `Genotype`. In general, the `Genotype` can be created with 1 to n chromosomes.\n\n2. Once this is done, the fitness function, which should be maximized, can be defined. Utilizing the new language features introduced in Java 8, we simply write a private static method, which takes the genotype we defined and calculates its fitness value. If we want to use the optimized bit-counting method, `bitCount()`, we have to cast the `Chromosome\u003CBitGene>` class to the actual used `BitChromosome` class. Since we know for sure that we created the Genotype with a `BitChromosome`, this can be done safely. A reference to the eval method is then used as fitness function and passed to the `Engine.build` method.\n\n3. In the third step we are creating the evolution `Engine`, which is responsible for changing, respectively evolving, a given population. The `Engine` is highly configurable and takes parameters for controlling the evolutionary and the computational environment. For changing the evolutionary behavior, you can set different alterers and selectors. By changing the used `Executor` service, you control the number of threads; the Engine is allowed to use. A new `Engine` instance can only be created via its builder, which is created by calling the `Engine.builder` method.\n\n4. In the last step, we can create a new `EvolutionStream` from our `Engine`. The `EvolutionStream` is the model or view of the evolutionary process. It serves as a »process handle« and also allows you, among other things, to control the termination of the evolution. In our example, we simply truncate the stream after 100 generations. If you don't limit the stream, the `EvolutionStream` will not terminate and run forever. Since the `EvolutionStream` extends the `java.util.stream.Stream` interface, it integrates smoothly with the rest of the Java Stream API. The final result, the best `Genotype` in our example, is then collected with one of the predefined collectors of the `EvolutionResult` class.\n\n\n### Evolving images\n\nThis example tries to approximate a given image by semitransparent polygons.  It comes with a Swing UI, where you can immediately start your own experiments. After compiling the sources with\n\n    $ .\u002Fgradlew compileTestJava\n\nyou can start the example by calling\n\n    $ .\u002Fjrun io.jenetics.example.image.EvolvingImages\n\n![Evolving images](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjenetics_jenetics_readme_f83fa753a3ef.png)\n\nThe previous image shows the GUI after evolving the default image for about 4,000 generations. With the »Open« button, it is possible to load other images for polygonization. The »Save« button allows storing polygonized images in PNG format to disk. At the button of the UI, you can change some GA parameters of the example.\n\n\n## Projects using Jenetics\n\n* \u003Ca href=\"https:\u002F\u002Fspear-project.eu\u002F\">\u003Cb>SPEAR\u003C\u002Fb>:\u003C\u002Fa> SPEAR (Smart Prognosis of Energy with Allocation of Resources) created an extendable platform for energy and efficiency optimizations of production systems.\n* \u003Ca href=\"https:\u002F\u002Frenaissance.dev\u002F\">\u003Cb>Renaissance Suite\u003C\u002Fb>:\u003C\u002Fa> Renaissance is a modern, open, and diversified benchmark suite for the JVM, aimed at testing JIT compilers, garbage collectors, profilers, analyzers and other tools.\n* \u003Ca href=\"http:\u002F\u002Fwww.eclipse.org\u002Fapp4mc\u002F\">\u003Cb>APP4MC\u003C\u002Fb>:\u003C\u002Fa> Eclipse APP4MC is a platform for engineering embedded multi- and many-core software systems.\n\n## Blogs and articles\n\n* \u003Ca href=\"https:\u002F\u002Fdieschwalbe.de\u002Fschwalbeaktuell.htm\">Schachprobleme komponieren mit evolutionären Algorithmen\u003C\u002Fa>, by \u003Cem>Jakob Leck\u003C\u002Fem>, Dec 2023, Die Schwalbe 324-2, pp. 373-380. Composition and solving chess problems with a greater number of peaces than usual. Instead of a brute force approach, a GA is used solving the problems (German).\n* \u003Ca href=\"https:\u002F\u002Fcraftcodecrew.com\u002Fsolving-the-knapsack-problem-with-the-jenetics-library\u002F\">Solving the Knapsack Problem with the Jenetics Library\u003C\u002Fa>, by \u003Cem>Craftcode Crew\u003C\u002Fem>, May 13, 2021\n* \u003Ca href=\"http:\u002F\u002Fwww.fx361.com\u002Fpage\u002F2018\u002F1126\u002F4534731.shtml\">一种基于Jenetics的遗传算法程序设计\u003C\u002Fa>, \u003Cem>电脑知识与技术 2018年22期 by 王康\u003C\u002Fem>, Nov. 26. 2018\n* \u003Ca href=\"http:\u002F\u002Fwww.baeldung.com\u002Fjenetics\">Introduction to Jenetics Library\u003C\u002Fa>, by \u003Cem>baeldung\u003C\u002Fem>, April 11. 2017\n* \u003Ca href=\"http:\u002F\u002Fblog.takipi.com\u002Fhow-to-solve-tough-problems-using-genetic-algorithms\u002F\">How to Solve Tough Problems Using Genetic Algorithms\u003C\u002Fa>, by \u003Cem>Tzofia Shiftan\u003C\u002Fem>, April 6. 2017\n* \u003Ca href=\"http:\u002F\u002Ffxapps.blogspot.co.at\u002F2017\u002F01\u002Fgenetic-algorithms-with-java.html\">Genetic algorithms with Java\u003C\u002Fa>, by \u003Cem>William Antônio\u003C\u002Fem>, January 10. 2017\n* \u003Ca href=\"http:\u002F\u002Fjdm.kr\u002Fblog\u002F135\">Jenetics 설치 및 예제\u003C\u002Fa>, by \u003Cem>JDM\u003C\u002Fem>, May 8. 2015\n* \u003Ca href=\"http:\u002F\u002Fjdm.kr\u002Fblog\u002F104\">유전 알고리즘 (Genetic Algorithms)\u003C\u002Fa>, by \u003Cem>JDM\u003C\u002Fem>, April 2. 2015\n\n## Citations\n\n\u003Cdetails>\n\u003Csummary>\nMarko Šmid, Miha Ravber. \u003Ca href=\"https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.softx.2026.102607\"> GIANT: General intelligent AgeNt trainer.\u003C\u002Fa> \u003Cem>SoftwareX, Volume 34, \u003C\u002Fem> June 2026.\n\n...\n\u003C\u002Fsummary>\n\n1) Marko Šmid, Miha Ravber. \u003Ca href=\"https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.softx.2026.102607\"> GIANT: General intelligent AgeNt trainer.\u003C\u002Fa> \u003Cem>SoftwareX, Volume 34, \u003C\u002Fem> June 2026.\n2) Milan Cugurovic, Aleksandar Prokopec, Boris Spasojevic, Vojin Jovanovic, and Milena Vujošević Janičić. \u003Ca href=\"https:\u002F\u002Fdoi.org\u002F10.1145\u002F3771775.3786276\"> GraalMHC: ML-Based Method-Hotness Classification for Binary-Size Reduction in Optimizing Compilers.\u003C\u002Fa> \u003Cem>In Proceedings of the 35th ACM SIGPLAN International Conference on Compiler Construction (CC '26). Association for Computing Machinery, New York, NY, USA, 1–13. \u003C\u002Fem> Jan. 2026.\n3) J. Daniel Dávalos Soto et al. \u003Ca href=\"https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F11298634\"> Seasonal Reconfiguration of Electrical Distribution Systems to Mitigate the Impact of Electric Vehicle Charging.\u003C\u002Fa> \u003Cem>IEEE Access, vol. 13, pp. 212193-212212. \u003C\u002Fem> Dec 2025.\n4) Ritwik Murali, Ashwin Narayanan Sivamani, Abhinav Ramakrishnan, Hariharan Arul, and Ananya R. \u003Ca href=\"https:\u002F\u002Fdoi.org\u002F10.1145\u002F3712255.3726652\"> Evolve On Click (EvOC) - An Intuitive Web Platform to Collaboratively Implement, Execute, and Visualize Evolutionary Algorithms.\u003C\u002Fa> \u003Cem>Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '25 Companion). Association for Computing Machinery, New York, NY, USA, 147–150.\u003C\u002Fem> Aug. 2025.\n5) Hotz, M., Malburg, L., Bergmann, R. \u003Ca href=\"https:\u002F\u002Fdoi.org\u002F10.1007\u002F978-3-031-96559-3_16\"> Advanced Search Techniques for Determining Optimal Sequences of Adaptation Rules in Process-Oriented Case-Based Reasoning.\u003C\u002Fa> \u003Cem>Case-Based Reasoning Research and Development. ICCBR 2025. Lecture Notes in Computer Science(), vol 15662. Springer, Cham.\u003C\u002Fem> June 2025.\n6) C. Chen, B. Dolan-Gavitt, Z. Lin. \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2506.10323\"> ELFuzz: Efficient Input Generation via LLM-driven Synthesis Over Fuzzer Space.\u003C\u002Fa> \u003Cem>USENIX Security'25 Cycle 2.\u003C\u002Fem> June 2025.\n7) Sathis Kumar K; Janani T; Karpagavadivu K; Raihana A; Meenalochini M. \u003Ca href=\"https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F11019139\"> Effective Task Scheduling based on Candidate Optimization Algorithm (COA) in Heterogeneous NoC-Based MPSoC.\u003C\u002Fa> \u003Cem>   2025 Fourth International Conference on Smart Technologies, Communication and Robotics (STCR), Sathyamangalam, India, 2025, pp. 1-8.\u003C\u002Fem> June 2025.\n8) Fabian Mastenbroek, Tiziano De Matteis, Vincent van Beek, Alexandru Iosup. \u003Ca href=\"https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.future.2024.107702\"> RADiCe: A Risk Analysis Framework for Data Centers.\u003C\u002Fa> \u003Cem>  Future Generation Computer Systems Volume 166.\u003C\u002Fem> May 2025.\n9) Toderean, L., Daian, M., Cioara, T. et al. \u003Ca href=\"https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-025-96443-3\"> Heuristic based federated learning with adaptive hyperparameter tuning for households energy prediction.\u003C\u002Fa> \u003Cem>  Sci Rep 15, 12564.\u003C\u002Fem> April 2025.\n10) Rui Menoita, Sara Silva. \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fpdf\u002F2504.05418\"> Evolving Financial Trading Strategies with Vectorial Genetic Programming.\u003C\u002Fa> \u003Cem>  arXiv preprint arXiv:2504.05418.\u003C\u002Fem> April 2025.\n11) Sandhya Avasthi, Shrishti Garg, Suman Lata Tripathi, Ritu Chauhan. \u003Ca href=\"https:\u002F\u002Fdoi.org\u002F10.1016\u002FB978-0-443-29162-3.00001-0\"> Metaheuristics algorithms: Fundamental aspects and applications in optimization problems.\u003C\u002Fa> \u003Cem>  Metaheuristics-Based Materials Optimization, Woodhead Publishing.\u003C\u002Fem> Feb. 2025.\n12) Nils Japke, Martin Grambow, Christoph Laaber, David Bermbach. \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2501.12878\"> μOpTime: Statically Reducing the Execution Time of Microbenchmark Suites Using Stability Metrics.\u003C\u002Fa> \u003Cem>  ACM Transactions on Software Engineering and Methodolog.\u003C\u002Fem> Jan. 2025.\n13) Fahimeh Bahrami, Rodolfo Jordao, Ingo Sander, Ingemar Söderquist. \u003Ca href=\"https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3714412\"> OBridging the Abstraction Gap: A Systematic Approach to Rule-Based Transformational Design for Embedded Systems.\u003C\u002Fa> \u003Cem>  ACM Transactions on Embedded Computing Systems. \u003C\u002Fem> Jan. 2025.\n14) Vincent A. 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Scott, Sean Luke. \u003Ca href=\"https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3319619.3326865\">ECJ at 20: toward a general metaheuristics toolkit. \u003C\u002Fa> \u003Cem>GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Pages 1391–1398. \u003C\u002Fem>\u003C\u002Fa> July 2019.\n101) Francisco G. Montoya and Raúl Baños Navarro (Eds.). \u003Ca href=\"https:\u002F\u002Fwww.mdpi.com\u002Fbooks\u002Fpdfview\u002Fbook\u002F1450\">Optimization Methods Applied to Power Systems, Volume 2. \u003C\u002Fa> \u003Cem>MDPI Books, ISBN 978-3-03921-156-2. \u003C\u002Fem>\u003C\u002Fa> July 2019.\n102) Höttger, Robert & Ki, Junhyung & Bui, Bao & Igel, Burkhard & Spinczyk, Olaf. \u003Ca href=\"https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F335137686_CPU-GPU_Response_Time_and_Mapping_Analysis_for_High-Performance_Automotive_Systems\">CPU-GPU Response Time and Mapping Analysis for High-Performance Automotive Systems. \u003C\u002Fa> \u003Cem>10th International Workshop on Analysis Tools and Methodologies for Embedded and Real-time Systems (WATERS) co-located with the 31st Euromicro Conference on Real-Time Systems (ECRTS'19). \u003C\u002Fem>\u003C\u002Fa> July 2019.\n103) Maxime Cordy, Steve Muller, Mike Papadakis, and Yves Le Traon. \u003Ca href=\"http:\u002F\u002Fdelivery.acm.org\u002F10.1145\u002F3340000\u002F3330580\u002Fissta19main-p399-p.pdf?ip=84.114.111.7&id=3330580&acc=OPEN&key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E6D218144511F3437&__acm__=1563299816_46b771752984b933c8c119b7f7d81805\">Search-based test and improvement of machine-learning-based anomaly detection systems. \u003C\u002Fa> \u003Cem>Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2019). ACM, New York, NY, USA, 158-168. \u003C\u002Fem>\u003C\u002Fa> July 2019.\n104) Michael Vistein, Jan Faber, Clemens Schmidt-Eisenlohr, Daniel Reiter. \u003Ca href=\"https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.promfg.2020.01.220\">Automated Handling of Auxiliary Materials using a Multi-Kinematic Gripping System. \u003C\u002Fa> \u003Cem>Procedia Manufacturing Volume 38, 2019, Pages 1276-1283. \u003C\u002Fem>\u003C\u002Fa> June 2019.\n105) Nikolaos Nikolakis, Ioannis Stathakis, Sotirios Makris. \u003Ca href=\"https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.procir.2019.03.153\">On an evolutionary information system for personalized support to plant operators. \u003C\u002Fa> \u003Cem>52nd CIRP Conference on Manufacturing Systems (CMS), Ljubljana, Slovenia. \u003C\u002Fem>\u003C\u002Fa> June 2019.\n106) Michael Trotter, Timothy Wood and Jinho Hwang. \u003Ca href=\"http:\u002F\u002Ffaculty.cs.gwu.edu\u002Ftimwood\u002Fpapers\u002F19-ICAC-storm.pdf\">Forecasting a Storm: Divining Optimal Configurations using Genetic Algorithms and Supervised Learning. \u003C\u002Fa> \u003Cem>13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2019). \u003C\u002Fem>\u003C\u002Fa> June 2019.\n107) Krawczyk, Lukas & Bazzal, Mahmoud & Prasath Govindarajan, Ram & Wolff, Carsten. \u003Ca href=\"https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F334084554_An_analytical_approach_for_calculating_end-to-end_response_times_in_autonomous_driving_applications\">An analytical approach for calculating end-to-end response times in autonomous driving applications. \u003C\u002Fa> \u003Cem>10th International Workshop on Analysis Tools and Methodologies for Embedded and Real-time Systems (WATERS 2019). \u003C\u002Fem>\u003C\u002Fa> June 2019.\n108) Rodolfo Ayala Lopes, Thiago Macedo Gomes, and Alan Robert Resende de Freitas. \u003Ca href=\"http:\u002F\u002Fdelivery.acm.org\u002F10.1145\u002F3330000\u002F3326828\u002Fp1366-lopes.pdf?ip=84.114.111.7&id=3326828&acc=OPEN&key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E6D218144511F3437&__acm__=1563021092_5e8cda0c5ddddb14d4f5e9e3bd610a44\">A symbolic evolutionary algorithm software platform. \u003C\u002Fa> \u003Cem>Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '19). \u003C\u002Fem>\u003C\u002Fa> July 2019.\n109) Aleksandar Prokopec, Andrea Rosà, David Leopoldseder, Gilles Duboscq, Petr Tůma, Martin Studener, Lubomír Bulej, Yudi Zheng, Alex Villazón, Doug Simon, Thomas Würthinger, Walter Binder. \u003Ca href=\"https:\u002F\u002Frenaissance.dev\u002Fresources\u002Fdocs\u002Frenaissance-suite.pdf\">Renaissance: Benchmarking Suite for Parallel Applications on the JVM. \u003C\u002Fa> \u003Cem>PLDI ’19, Phoenix, AZ, USA. \u003C\u002Fem>\u003C\u002Fa> June 2019.\n110) Robert Höttger, Lukas Krawczyk, Burkhard Igel, Olaf Spinczyk. \u003Ca href=\"http:\u002F\u002F2019.rtas.org\u002Fwp-content\u002Fuploads\u002F2019\u002F04\u002FRTAS19_BP_proceedings.pdf#page=23\">Memory Mapping Analysis for Automotive Systems. \u003C\u002Fa> \u003Cem>Brief Presentations Proceedings (RTAS 2019). \u003C\u002Fem>\u003C\u002Fa> Apr. 2019.\n111) Al Akkad, M. A., & Gazimzyanov, F. F. \u003Ca href=\"http:\u002F\u002Fizdat.istu.ru\u002Findex.php\u002FISM\u002Farticle\u002Fview\u002F4317\">AUTOMATED SYSTEM FOR EVALUATING 2D-IMAGE COMPOSITIONAL CHARACTERISTICS: CONFIGURING THE MATHEMATICAL MODEL.\u003C\u002Fa> \u003Cem>Intellekt. Sist. Proizv., 17(1), 26-33. doi: 10.22213\u002F2410-9304-2019-1-26-33. \u003C\u002Fem>\u003C\u002Fa> Apr. 2019.\n112) Alcayde, A.; Baños, R.; Arrabal-Campos, F.M.; Montoya, F.G. \u003Ca href=\"https:\u002F\u002Fwww.mdpi.com\u002F1996-1073\u002F12\u002F7\u002F1270\">Optimization of the Contracted Electric Power by Means of Genetic Algorithms.\u003C\u002Fa> \u003Cem>Energies, Volume 12, Issue 7, \u003C\u002Fem>\u003C\u002Fa> Apr. 2019.\n113) Abdul Sahli Fakharudin, Norazwina Zainol, Zulsyazwan Ahmad Khushairi. \u003Ca href=\"https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3323716.3323737\">Modelling and Optimisation of Oil Palm Trunk Core Biodelignification using Neural Network and Genetic Algorithm.\u003C\u002Fa> \u003Cem>IEEA '19: Proceedings of the 8th International Conference on Informatics, Environment, Energy and Applications; Pages 155–158, \u003C\u002Fem>\u003C\u002Fa> Mar. 2019.\n114) Aleksandar Prokopec, Andrea Rosà, David Leopoldseder, Gilles Duboscq, Petr Tůma, Martin Studener, Lubomír Bulej, Yudi Zheng, Alex Villazón, Doug Simon, Thomas Wuerthinger, Walter Binder. \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.10267.pdf\">On Evaluating the Renaissance Benchmarking Suite: Variety, Performance, and Complexity.\u003C\u002Fa> \u003Cem>Cornell University: Programming Languages, \u003C\u002Fem>\u003C\u002Fa> Mar. 2019.\n115) S. Appel, W. Geithner, S. Reimann, M Sapinski, R. Singh, D. M. Vilsmeier \u003Ca href=\"https:\u002F\u002Fwww.researchgate.net\u002Fprofile\u002FSabrina_Appel\u002Fpublication\u002F330934110_OPTIMIZATION_OF_HEAVY-ION_SYNCHROTRONS_USING_NATURE-INSPIRED_ALGORITHMS_AND_MACHINE_LEARNING\u002Flinks\u002F5c5c425b299bf1d14cb33546\u002FOPTIMIZATION-OF-HEAVY-ION-SYNCHROTRONS-USING-NATURE-INSPIRED-ALGORITHMS-AND-MACHINE-LEARNING.pdf\">OPTIMIZATION OF HEAVY-ION SYNCHROTRONS USINGNATURE-INSPIRED ALGORITHMS AND MACHINE LEARNING.\u003C\u002Fa>\u003Cem>\u003Ca href=\"https:\u002F\u002Fbt.pa.msu.edu\u002FICAP18\u002Findex.html\">13th Int. Computational Accelerator Physics Conf.\u003C\u002Fa>, \u003C\u002Fem>\u003C\u002Fa> Feb. 2019.\n116) Saad, Christian, Bernhard Bauer, Ulrich R Mansmann, and Jian Li. \u003Ca href=\"https:\u002F\u002Fjournals.sagepub.com\u002Fdoi\u002F10.1177\u002F1177932218818458\">AutoAnalyze in Systems Biology.\u003C\u002Fa> \u003Cem>Bioinformatics and Biology Insights, \u003C\u002Fem>\u003C\u002Fa> Jan. 2019.\n117) Gandeva Bayu Satrya, Soo Young Shin. \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fpdf\u002F1812.01201.pdf\">Evolutionary Computing Approach to Optimize Superframe Scheduling on Industrial Wireless Sensor Networks.\u003C\u002Fa> \u003Cem>Cornell University, \u003C\u002Fem>\u003C\u002Fa> Dec. 2018.\n118) H.R. Maier, S. Razavi, Z. Kapelan, L.S. Matott, J. 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Teppan and Giacomo Da Col. \u003Ca href=\"http:\u002F\u002Fceur-ws.org\u002FVol-2252\u002Fpaper4.pdf\">Automatic Generation of Dispatching Rules for Large Job Shops by Means of Genetic Algorithms.\u003C\u002Fa> \u003Cem>CIMA 2018, International Workshop on Combinations of Intelligent Methods and Applications, \u003C\u002Fem>\u003C\u002Fa> Nov. 2018.\n120) Pasquale Salzaa, Filomena Ferrucci. \u003Ca href=\"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0167739X17324147\">Speed up genetic algorithms in the cloud using software containers.\u003C\u002Fa> \u003Cem>Future Generation Computer Systems, \u003C\u002Fem>\u003C\u002Fa> Oct. 2018.\n121) Ghulam Mubashar Hassan and Mark Reynolds. \u003Ca href=\"https:\u002F\u002Feasychair.org\u002Fpublications\u002Fopen\u002FGRLP\">Genetic Algorithms for Scheduling and Optimization of Ore Train Networks.\u003C\u002Fa> \u003Cem>GCAI-2018. 4th Global Conference on Artificial Intelligence, \u003C\u002Fem>\u003C\u002Fa> Sep. 2018.\n122) Drezewski, Rafal & Kruk, Sylwia & Makowka, Maciej. 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[Die Evolution im Algorithmus - Teil 2: Multikriterielle Optimierung und Architekturerkennung.](http:\u002F\u002Fwww.buschmais.de\u002Fwp-content\u002Fuploads\u002F2018\u002F06\u002FDie-Evolution-im-Algorithmus_Teil2_JS_03_18.pdf) \u003Ca href=\"https:\u002F\u002Fwww.sigs-datacom.de\u002Fdigital\u002Fjavaspektrum\u002F\">\u003Cem>JavaSPEKTRUM 03\u002F2018, pp 66–69, \u003C\u002Fem>\u003C\u002Fa> May 2018.\n126) W. Geithner, Z. Andelkovic, S. Appel, O. Geithner, F. Herfurth, S. Reimann, G. Vorobjev, F. Wilhelmstötter. [Genetic Algorithms for Machine Optimization in the Fair Control System Environment.](http:\u002F\u002Faccelconf.web.cern.ch\u002FAccelConf\u002Fipac2018\u002Fpapers\u002Fthpml028.pdf)\u003Cem> [The 9th International Particle Accelerator Conference (IPAC'18)](https:\u002F\u002Fipac18.org\u002Fwelcome\u002F), \u003C\u002Fem>\u003C\u002Fa> May 2018.\n127) Stephan Pirnbaum. 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Johnson. \u003Ca href=\"https:\u002F\u002Flink.springer.com\u002Fprotocol\u002F10.1007\u002F978-1-4939-7386-6_13\">From Raw Data to Protein Backbone Chemical Shifts Using NMRFx Processing and NMRViewJ Analysis. \u003C\u002Fa> \u003Cem>Protein NMR: Methods and Protocols, pp. 257--310, Springer New York, \u003C\u002Fem> Nov. 2017.\n130) Cuadra P., Krawczyk L., Höttger R., Heisig P., Wolff C. \u003Ca href=\"https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-67642-5_30\">Automated Scheduling for Tightly-Coupled Embedded Multi-core Systems Using Hybrid Genetic Algorithms. \u003C\u002Fa> \u003Cem>Information and Software Technologies: 23rd International Conference, ICIST 2017, Druskininkai, Lithuania.\u003C\u002Fem> Communications in Computer and Information Science, vol 756. 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Linebarger, Richard J. Detry, Robert J. Glass, Walter E. Beyeler, Arlo L. Ames, Patrick D. Finley, S. Louise Maffitt. \u003Ca href=\"http:\u002F\u002Fprod.sandia.gov\u002Ftechlib\u002Faccess-control.cgi\u002F2012\u002F121117.pdf\"> \u003Cem>Complex Adaptive Systems of Systems Engineering Environment Version 1.0.  \u003C\u002Fem>\u003C\u002Fa> \u003Ca href=\"http:\u002F\u002Fwww.sandia.gov\u002FCasosEngineering\u002F\">SAND REPORT\u003C\u002Fa>, Feb. 2012.\n\u003C\u002Fdetails>\n\n## Release notes\n\n### [9.0.0](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Freleases\u002Ftag\u002Fv9.0.0)\n\n#### Improvements\n\n* Update Java 25 and optimize code for new Java version.\n* [#917](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F917): `ScopedValue` for `RandomRegistry` class.\n* [#940](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F940): Remove deprecated API.\n* [#955](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F955): Make `IntStream` counting more robust.\n\n_[All Release Notes](RELEASE_NOTES.md)_\n\n## License\n\nThe library is licensed under the [Apache License, Version 2.0](http:\u002F\u002Fwww.apache.org\u002Flicenses\u002FLICENSE-2.0.html).\n\n\tCopyright 2007-2026 Franz Wilhelmstötter\n\n\tLicensed under the Apache License, Version 2.0 (the \"License\");\n\tyou may not use this file except in compliance with the License.\n\tYou may obtain a copy of the License at\n\n\thttp:\u002F\u002Fwww.apache.org\u002Flicenses\u002FLICENSE-2.0\n\n\tUnless required by applicable law or agreed to in writing, software\n\tdistributed under the License is distributed on an \"AS IS\" BASIS,\n\tWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n\tSee the License for the specific language governing permissions and\n\tlimitations under the License.\n\n\n## Used software\n\n\n\u003Ca href=\"https:\u002F\u002Fwww.jetbrains.com\u002Fidea\u002F\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjenetics_jenetics_readme_1710e9ac5bf0.png\" alt=\"IntelliJ\" height=\"100\"\u002F>\u003C\u002Fa>\n\n\u003Ca href=\"https:\u002F\u002Fwww.syntevo.com\u002Fsmartgit\u002F\">\u003Cimg src=\"https:\u002F\u002Fwww.syntevo.com\u002Fassets\u002Fimages\u002Flogos\u002Fsmartgit-8c1aa1e2.svg\" alt=\"SmartGit\" height=\"100\"\u002F>\u003C\u002Fa>\n","# Jenetics\n\n[![构建状态](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Factions\u002Fworkflows\u002Fgradle.yml\u002Fbadge.svg)](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Factions?query=branch%3Amaster)\n[![Maven Central 版本](https:\u002F\u002Fimg.shields.io\u002Fmaven-central\u002Fv\u002Fio.jenetics\u002Fjenetics?color=green)](https:\u002F\u002Fcentral.sonatype.com\u002Fartifact\u002Fio.jenetics\u002Fjenetics)\n[![Javadoc](https:\u002F\u002Fwww.javadoc.io\u002Fbadge\u002Fio.jenetics\u002Fjenetics.svg)](http:\u002F\u002Fwww.javadoc.io\u002Fdoc\u002Fio.jenetics\u002Fjenetics)\n\n**Jenetics** 是一个基于现代 Java 编写的 **遗传算法**、**进化算法**、**语法进化**、**遗传编程** 和 **多目标优化** 库。它采用清晰的模块化设计，将算法中的各个概念明确分离，例如 `基因`、`染色体`、`基因型`、`表现型`、`种群` 以及适应度 `函数`。**Jenetics** 允许您在无需调整适应度函数的情况下，直接对其进行最小化或最大化操作。与其他遗传算法实现不同，该库使用进化流（`EvolutionStream`）的概念来执行进化步骤。由于 `EvolutionStream` 实现了 Java Stream 接口，因此它可以与 Java Stream API 的其他部分无缝协作。\n\n**其他语言**\n\n* [**Jenetics.Net**](https:\u002F\u002Fgithub.com\u002Frmeindl\u002Fjenetics.net)：用 C# 编写的实验性 .NET Core 版本的基础库移植。\n* [**Helisa**](https:\u002F\u002Fgithub.com\u002Fsoftwaremill\u002Fhelisa\u002F)：Jenetics 库的 Scala 封装。\n\n## 星标历史\n\n[![星标历史图表](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjenetics_jenetics_readme_91d1ee7c84d1.png)](https:\u002F\u002Fwww.star-history.com\u002F#jenetics\u002Fjenetics&Date)\n\n## 文档\n\n该库拥有完整的文档（[javadoc](https:\u002F\u002Fjenetics.io\u002Fjavadoc\u002Fcombined\u002F9.0\u002Findex.html)）和用户手册（[pdf](http:\u002F\u002Fjenetics.io\u002Fmanual\u002Fmanual-9.0.0.pdf)）。\n\n## 构建 Jenetics\n\n**Jenetics** 至少需要 **Java 25** 才能编译和运行。\n\n从 GitHub 克隆主分支：\n\n    $ git clone https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics.git \u003Cbuilddir>\n\nJenetics 使用 [Gradle](http:\u002F\u002Fwww.gradle.org\u002Fdownloads) 作为构建系统，并将源代码组织成多个子项目（模块）。每个子项目都位于其各自的子目录中：\n\n**已发布的项目**\n\n以下项目\u002F模块也已发布到 Maven 中心仓库：\n\n* **[jenetics](jenetics)** [![Javadoc](https:\u002F\u002Fwww.javadoc.io\u002Fbadge\u002Fio.jenetics\u002Fjenetics.svg)](http:\u002F\u002Fwww.javadoc.io\u002Fdoc\u002Fio.jenetics\u002Fjenetics)：该项目包含 Jenetics 核心模块的源代码和测试。\n* **[jenetics.ext](jenetics.ext)** [![Javadoc](https:\u002F\u002Fwww.javadoc.io\u002Fbadge\u002Fio.jenetics\u002Fjenetics.svg)](http:\u002F\u002Fwww.javadoc.io\u002Fdoc\u002Fio.jenetics\u002Fjenetics.ext)：该模块包含额外的非标准遗传算法操作和数据类型。它还包含用于解决多目标问题（MOEA）和进行语法进化的类。\n* **[jenetics.prog](jenetics.prog)** [![Javadoc](https:\u002F\u002Fwww.javadoc.io\u002Fbadge\u002Fio.jenetics\u002Fjenetics.svg)](http:\u002F\u002Fwww.javadoc.io\u002Fdoc\u002Fio.jenetics\u002Fjenetics.prog)：这些模块包含用于执行遗传编程（GP）的类。它们可以与现有的 `EvolutionStream` 和进化引擎无缝集成。\n* **[jenetics.xml](jenetics.xml)** [![Javadoc](https:\u002F\u002Fwww.javadoc.io\u002Fbadge\u002Fio.jenetics\u002Fjenetics.svg)](http:\u002F\u002Fwww.javadoc.io\u002Fdoc\u002Fio.jenetics\u002Fjenetics.xml)：这是用于 _Jenetics_ 基础数据结构的 XML 序列化模块。\n\n**未发布的模块**\n\n* **[jenetics.distassert](jenetics.distassert)**：该模块用于测试样本数据是否符合给定的统计分布。Jenetics 使用此模块来测试其遗传算法算子。\n* **[jenetics.example](jenetics.example)**：该模块包含核心模块的示例代码。\n* **[jenetics.doc](jenetics.doc)**：包含网站和手册的代码。\n* **[jenetics.tool](jenetics.tool)**：该模块包含用于集成测试和算法性能测试的类。它还用于创建遗传算法性能指标以及根据性能指标生成图表。\n\n要构建库，请进入 `\u003Cbuilddir>` 目录（或其中一个模块目录），然后运行以下任务之一：\n\n* **compileJava**：编译 Jenetics 源代码，并将编译后的类文件复制到 `\u003Cbuilddir>\u002F\u003Cmodule-dir>\u002Fbuild\u002Fclasses\u002Fmain` 目录。\n* **jar**：编译源代码并生成 JAR 文件。生成的工件会被复制到 `\u003Cbuilddir>\u002F\u003Cmodule-dir>\u002Fbuild\u002Flibs` 目录。\n* **javadoc**：生成 API 文档。Javadoc 存储在 `\u003Cbuilddir>\u002F\u003Cmodule-dir>\u002Fbuild\u002Fdocs` 目录中。\n* **test**：编译并运行单元测试。测试结果会打印到控制台，同时由 TestNG 生成的测试报告会被写入 `\u003Cbuilddir>\u002F\u003Cmodule-dir>` 目录。\n* **clean**：删除 `\u003Cbuilddir>\u002Fbuild\u002F*` 目录，并移除所有生成的工件。\n\n要从源代码构建库的 JAR 文件，请执行以下命令：\n\n    $ cd \u003Cbuild-dir>\n    $ .\u002Fgradlew jar\n\n\n## 示例\n\n### 你好，世界（统计1的个数）\n\n最小化的进化引擎设置需要一个基因型工厂 `Factory\u003CGenotype\u003C?>>` 和一个适应度函数 `Function`。`Genotype` 实现了 `Factory` 接口，因此可以用作创建初始种群和生成新的随机基因型的原型。\n\n```java\nimport io.jenetics.BitChromosome;\nimport io.jenetics.BitGene;\nimport io.jenetics.Genotype;\nimport io.jenetics.engine.Engine;\nimport io.jenetics.engine.EvolutionResult;\nimport io.jenetics.util.Factory;\n\npublic class HelloWorld {\n    \u002F\u002F 2.) 定义适应度函数。\n    private static Integer eval(Genotype\u003CBitGene> gt) {\n        return gt.chromosome()\n            .as(BitChromosome.class)\n            .bitCount();\n    }\n\n    public static void main(String[] args) {\n        \u002F\u002F 1.) 定义适合该问题的基因型（工厂）。\n        Factory\u003CGenotype\u003CBitGene>> gtf =\n            Genotype.of(BitChromosome.of(10, 0.5));\n\n        \u002F\u002F 3.) 创建执行环境。\n        Engine\u003CBitGene, Integer> engine = Engine\n            .builder(HelloWorld::eval, gtf)\n            .build();\n\n        \u002F\u002F 4.) 启动执行（进化），并收集结果。\n        Genotype\u003CBitGene> result = engine.stream()\n            .limit(100)\n            .collect(EvolutionResult.toBestGenotype());\n\n        System.out.println(\"你好，世界:\\n\" + result);\n    }\n}\n```\n\n与其他遗传算法实现不同，该库使用进化流（`EvolutionStream`）的概念来执行进化步骤。由于 `EvolutionStream` 实现了 Java Stream 接口，它可以与 Java 流式 API 的其他部分无缝协作。现在让我们仔细看看上面的代码，并逐步讨论这个简单的程序：\n\n1. 在设置一个新的进化引擎时，最具有挑战性的部分可能是将问题域转换为合适的基因型（工厂）表示。在我们的示例中，我们希望统计一个 `BitChromosome` 中1的个数。由于我们只统计一个染色体中的1，因此我们只向基因型中添加了一个 `BitChromosome`。一般来说，基因型可以由1到n个染色体组成。\n\n2. 一旦完成这一步，就可以定义要最大化的适应度函数。利用 Java 8 引入的新语言特性，我们只需编写一个私有静态方法，该方法接受我们定义的基因型并计算其适应度值。如果我们想使用优化的位计数方法 `bitCount()`，就必须将 `Chromosome\u003CBitGene>` 类强制转换为实际使用的 `BitChromosome` 类。由于我们确定基因型是由 `BitChromosome` 创建的，因此可以安全地进行此操作。然后将对 `eval` 方法的引用用作适应度函数，并传递给 `Engine.build` 方法。\n\n3. 在第三步中，我们创建了进化引擎，它负责改变或进化给定的群体。引擎具有高度可配置性，可以设置参数来控制进化和计算环境。为了改变进化行为，可以设置不同的变异算子和选择算子。通过更改所使用的 `Executor` 服务，可以控制引擎允许使用的线程数量。新的引擎实例只能通过其构建器创建，而构建器是通过调用 `Engine.builder` 方法创建的。\n\n4. 在最后一步中，我们可以从引擎中创建一个新的进化流。进化流是进化过程的模型或视图，它充当“进程句柄”，并且还允许您控制进化是否终止等。在我们的示例中，我们简单地将流截断为100代。如果不限制流，进化流将不会终止并会永远运行。由于进化流扩展了 `java.util.stream.Stream` 接口，它能够与 Java 流式 API 的其余部分无缝集成。最终结果，即本例中的最佳基因型，随后通过 `EvolutionResult` 类预定义的收集器之一收集。\n\n### 进化图像\n\n这个示例尝试用半透明多边形近似给定的图像。它带有一个 Swing 界面，您可以立即开始自己的实验。在使用以下命令编译源代码后：\n\n    $ .\u002Fgradlew compileTestJava\n\n您可以通过以下命令启动该示例：\n\n    $ .\u002Fjrun io.jenetics.example.image.EvolvingImages\n\n![进化图像](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjenetics_jenetics_readme_f83fa753a3ef.png)\n\n上图显示的是在默认图像进化约4,000代后的 GUI。通过“打开”按钮，可以加载其他图像进行多边形化处理。而“保存”按钮则允许将多边形化的图像以 PNG 格式保存到磁盘上。在 UI 的底部，您可以更改该示例的一些遗传算法参数。\n\n\n## 使用 Jenetics 的项目\n\n* \u003Ca href=\"https:\u002F\u002Fspear-project.eu\u002F\">\u003Cb>SPEAR\u003C\u002Fb>:\u003C\u002Fa> SPEAR（资源分配下的智能能源预测）创建了一个可扩展的平台，用于生产系统的能源和效率优化。\n* \u003Ca href=\"https:\u002F\u002Frenaissance.dev\u002F\">\u003Cb>Renaissance Suite\u003C\u002Fb>:\u003C\u002Fa> Renaissance 是一套现代、开放且多样化的 JVM 基准测试套件，旨在测试 JIT 编译器、垃圾回收器、性能分析工具和其他工具。\n* \u003Ca href=\"http:\u002F\u002Fwww.eclipse.org\u002Fapp4mc\u002F\">\u003Cb>APP4MC\u003C\u002Fb>:\u003C\u002Fa> Eclipse APP4MC 是一个用于嵌入式多核和众核软件系统设计的平台。\n\n## 博客和文章\n\n* \u003Ca href=\"https:\u002F\u002Fdieschwalbe.de\u002Fschwalbeaktuell.htm\">用进化算法创作国际象棋问题\u003C\u002Fa>, 作者 \u003Cem>Jakob Leck\u003C\u002Fem>, 2023年12月，《Die Schwalbe》第324-2期，第373–380页。使用比通常更多的棋子来创作和解决国际象棋问题。与暴力搜索方法不同，这里使用遗传算法来解决问题（德语）。\n* \u003Ca href=\"https:\u002F\u002Fcraftcodecrew.com\u002Fsolving-the-knapsack-problem-with-the-jenetics-library\u002F\">使用 Jenetics 库解决背包问题\u003C\u002Fa>, 作者 \u003Cem>Craftcode Crew\u003C\u002Fem>, 2021年5月13日\n* \u003Ca href=\"http:\u002F\u002Fwww.fx361.com\u002Fpage\u002F2018\u002F1126\u002F4534731.shtml\">一种基于Jenetics的遗传算法程序设计\u003C\u002Fa>, 作者 \u003Cem>王康\u003C\u002Fem>, 《电脑知识与技术》2018年第22期，2018年11月26日\n* \u003Ca href=\"http:\u002F\u002Fwww.baeldung.com\u002Fjenetics\">Jenetics 库简介\u003C\u002Fa>, 作者 \u003Cem>baeldung\u003C\u002Fem>, 2017年4月11日\n* \u003Ca href=\"http:\u002F\u002Fblog.takipi.com\u002Fhow-to-solve-tough-problems-using-genetic-algorithms\u002F\">如何使用遗传算法解决难题\u003C\u002Fa>, 作者 \u003Cem>Tzofia Shiftan\u003C\u002Fem>, 2017年4月6日\n* \u003Ca href=\"http:\u002F\u002Ffxapps.blogspot.co.at\u002F2017\u002F01\u002Fgenetic-algorithms-with-java.html\">使用 Java 的遗传算法\u003C\u002Fa>, 作者 \u003Cem>William Antônio\u003C\u002Fem>, 2017年1月10日\n* \u003Ca href=\"http:\u002F\u002Fjdm.kr\u002Fblog\u002F135\">Jenetics 安装及示例\u003C\u002Fa>, 作者 \u003Cem>JDM\u003C\u002Fem>, 2015年5月8日\n* \u003Ca href=\"http:\u002F\u002Fjdm.kr\u002Fblog\u002F104\">遗传算法（Genetic Algorithms）\u003C\u002Fa>, 作者 \u003Cem>JDM\u003C\u002Fem>, 2015年4月2日\n\n## 引用\n\n\u003Cdetails>\n\u003Csummary>\n马尔科·施米德，米哈·拉夫贝尔。《GIANT：通用智能智能体训练器》。\u003Ca href=\"https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.softx.2026.102607\">DOI: 10.1016\u002Fj.softx.2026.102607\u003C\u002Fa>。《SoftwareX》，第34卷，2026年6月。\n\n...\n\u003C\u002Fsummary>\n\n1) Marko Šmid, Miha Ravber. \u003Ca href=\"https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.softx.2026.102607\"> GIANT: General intelligent AgeNt trainer.\u003C\u002Fa> \u003Cem>SoftwareX, Volume 34, \u003C\u002Fem> June 2026.\n2) Milan Cugurovic, Aleksandar Prokopec, Boris Spasojevic, Vojin Jovanovic, and Milena Vujošević Janičić. \u003Ca href=\"https:\u002F\u002Fdoi.org\u002F10.1145\u002F3771775.3786276\"> GraalMHC: ML-Based Method-Hotness Classification for Binary-Size Reduction in Optimizing Compilers.\u003C\u002Fa> \u003Cem>In Proceedings of the 35th ACM SIGPLAN International Conference on Compiler Construction (CC '26). 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Association for Computing Machinery, New York, NY, USA, 147–150.\u003C\u002Fem> Aug. 2025.\n5) Hotz, M., Malburg, L., Bergmann, R. \u003Ca href=\"https:\u002F\u002Fdoi.org\u002F10.1007\u002F978-3-031-96559-3_16\"> Advanced Search Techniques for Determining Optimal Sequences of Adaptation Rules in Process-Oriented Case-Based Reasoning.\u003C\u002Fa> \u003Cem>Case-Based Reasoning Research and Development. ICCBR 2025. Lecture Notes in Computer Science(), vol 15662. Springer, Cham.\u003C\u002Fem> June 2025.\n6) C. Chen, B. Dolan-Gavitt, Z. 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Springer, Singapore. \u003C\u002Fem>\u003C\u002Fa> May 2020.\n87) Sonya Voneva, Manar Mazkatli, Johannes Grohmann and Anne Koziolek. \u003Ca href=\"https:\u002F\u002Fsdqweb.ipd.kit.edu\u002Fpublications\u002Fpdfs\u002Fvoneva2020a.pdf\">Optimizing Parametric Dependencies forIncremental Performance Model Extraction.\u003C\u002Fa> \u003Cem>Karlsruhe Institute of Technology, Karlsruhe, Germany. \u003C\u002Fem>\u003C\u002Fa> April. 2020.\n88) Raúl Lara-Cabrera, Ángel González-Prieto, Fernando Ortega and Jesús Bobadilla. \u003Ca href=\"https:\u002F\u002Fwww.mdpi.com\u002F2076-3417\u002F10\u002F2\u002F675\">Evolving Matrix-Factorization-Based Collaborative Filtering Using Genetic Programming.\u003C\u002Fa> \u003Cem>MDPI, Applied Sciences. \u003C\u002Fem>\u003C\u002Fa> Feb. 2020.\n89) Humm B.G., Hutter M. \u003Ca href=\"https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-41913-4_12\">Learning Patterns for Complex Event Detection in Robot Sensor Data.\u003C\u002Fa> \u003Cem>Optimization and Learning. 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Springer, Singapore. \u003C\u002Fem>\u003C\u002Fa> Jan. 2020.\n91) Ricardo Pérez-Castillo, Francisco Ruiz, Mario Piattini. \u003Ca href=\"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS016792362030004X\">A decision-making support system for Enterprise Architecture Modelling. \u003C\u002Fa> \u003Cem>Decision Support Systems. \u003C\u002Fem>\u003C\u002Fa> Jan. 2020.\n92) Sabrina Appel, Wolfgang Geithner, Stephan Reimann, Mariusz Sapinski, Rahul Singh and Dominik Vilsmeier. \u003Ca href=\"https:\u002F\u002Fwww.worldscientific.com\u002Fdoi\u002Fabs\u002F10.1142\u002FS0217751X19420193\">Application of nature-inspired optimization algorithms and machine learning for heavy-ion synchrotrons. \u003C\u002Fa> \u003Cem>International Journal of Modern Physics A. \u003C\u002Fem>\u003C\u002Fa> Dec. 2019.\n93) O. M. Elzeki, M. F. Alrahmawy, Samir Elmougy. \u003Ca href=\"http:\u002F\u002Fwww.mecs-press.org\u002Fijisa\u002Fijisa-v11-n12\u002FIJISA-V11-N12-3.pdf\">A New Hybrid Genetic and Information Gain Algorithm for Imputing Missing Values in Cancer Genes Datasets. \u003C\u002Fa> \u003Cem>PInternational Journal of Intelligent Systems and Applications (IJISA), Vol.11, No.12, pp.20-33, DOI: 10.5815\u002Fijisa.2019.12.03. \u003C\u002Fem>\u003C\u002Fa> Dec. 2019.\n94) Oliver Strauß, Ahmad Almheidat and Holger Kett. \u003Ca href=\"https:\u002F\u002Fpdfs.semanticscholar.org\u002F0a91\u002Fc4e03a2acd8c295af398167edf7350ad0662.pdf\">Applying Heuristic and Machine Learning Strategies to ProductResolution. \u003C\u002Fa> \u003Cem>Proceedings of the 15th International Conference on Web Information Systems and Technologies (WEBIST 2019), pages 242-249. \u003C\u002Fem>\u003C\u002Fa> Nov. 2019.\n95) Yuanyuan Li, Stefano Carabelli, Edoardo Fadda, Daniele Manerba, Roberto Tadei1 and Olivier Terzo. \u003Ca href=\"http:\u002F\u002Fwww.orgroup.polito.it\u002Fmaterial\u002FDAUIN-ORO-2019-06.pdf\">Integration of Machine Learning and OptimizationTechniques for Flexible Job-Shop Rescheduling inIndustry 4.0. \u003C\u002Fa> \u003Cem>Politecnico di Torino, Operations Research and Optimization Group. \u003C\u002Fem>\u003C\u002Fa> Oct. 2019.\n96) Höttger R., Igel B., Spinczyk O. \u003Ca href=\"https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-030-30275-7_44\">Constrained Software Distribution for Automotive Systems. \u003C\u002Fa> \u003Cem>Communications in Computer and Information Science, vol 1078. \u003C\u002Fem>\u003C\u002Fa> Oct. 2019.\n97) Jin-wooLee, Gwangseon Jang, Hohyun Jung, Jae-Gil Lee, Uichin Lee. \u003Ca href=\"https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.pmcj.2019.101082\">Maximizing MapReduce job speed and reliability in the mobile cloud by optimizing task allocation. \u003C\u002Fa> \u003Cem>Pervasive and Mobile Computing. \u003C\u002Fem>\u003C\u002Fa> Oct. 2019.\n98) Krawczyk, Lukas, Mahmoud Bazzal, Ram Prasath Govindarajan and Carsten Wolff. \u003Ca href=\"https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8904877\">Model-Based Timing Analysis and Deployment Optimization for Heterogeneous Multi-core Systems using Eclipse APP4MC. \u003C\u002Fa> \u003Cem>2019 ACM\u002FIEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion: 44-53. \u003C\u002Fem>\u003C\u002Fa> Sep. 2019.\n99) Junio Cezar Ribeiro da Silva, Lorena Leão, Vinicius Petrucci, Abdoulaye Gamatié, Fernando MagnoQuintao Pereira. \u003Ca href=\"https:\u002F\u002Fhal-lirmm.ccsd.cnrs.fr\u002Flirmm-02281112\u002Fdocument\">Scheduling in Heterogeneous Architectures via Multivariate Linear Regression on Function Inputs. \u003C\u002Fa> \u003Cem>lirmm-02281112. \u003C\u002Fem>\u003C\u002Fa> Sep. 2019.\n100) Eric O. Scott, Sean Luke. \u003Ca href=\"https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3319619.3326865\">ECJ at 20: toward a general metaheuristics toolkit. \u003C\u002Fa> \u003Cem>GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Pages 1391–1398. \u003C\u002Fem>\u003C\u002Fa> July 2019.\n101) Francisco G. Montoya and Raúl Baños Navarro (Eds.). \u003Ca href=\"https:\u002F\u002Fwww.mdpi.com\u002Fbooks\u002Fpdfview\u002Fbook\u002F1450\">Optimization Methods Applied to Power Systems, Volume 2. \u003C\u002Fa> \u003Cem>MDPI Books, ISBN 978-3-03921-156-2. \u003C\u002Fem>\u003C\u002Fa> July 2019.\n102) Höttger, Robert & Ki, Junhyung & Bui, Bao & Igel, Burkhard & Spinczyk, Olaf. \u003Ca href=\"https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F335137686_CPU-GPU_Response_Time_and_Mapping_Analysis_for_High-Performance_Automotive_Systems\">CPU-GPU Response Time and Mapping Analysis for High-Performance Automotive Systems. \u003C\u002Fa> \u003Cem>10th International Workshop on Analysis Tools and Methodologies for Embedded and Real-time Systems (WATERS) co-located with the 31st Euromicro Conference on Real-Time Systems (ECRTS'19). \u003C\u002Fem>\u003C\u002Fa> July 2019.\n103) Maxime Cordy, Steve Muller, Mike Papadakis, and Yves Le Traon. \u003Ca href=\"http:\u002F\u002Fdelivery.acm.org\u002F10.1145\u002F3340000\u002F3330580\u002Fissta19main-p399-p.pdf?ip=84.114.111.7&id=3330580&acc=OPEN&key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E6D218144511F3437&__acm__=1563299816_46b771752984b933c8c119b7f7d81805\">Search-based test and improvement of machine-learning-based anomaly detection systems. \u003C\u002Fa> \u003Cem>Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2019). ACM, New York, NY, USA, 158-168. \u003C\u002Fem>\u003C\u002Fa> July 2019.\n104) Michael Vistein, Jan Faber, Clemens Schmidt-Eisenlohr, Daniel Reiter. \u003Ca href=\"https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.promfg.2020.01.220\">Automated Handling of Auxiliary Materials using a Multi-Kinematic Gripping System. \u003C\u002Fa> \u003Cem>Procedia Manufacturing Volume 38, 2019, Pages 1276-1283. \u003C\u002Fem>\u003C\u002Fa> June 2019.\n105) Nikolaos Nikolakis, Ioannis Stathakis, Sotirios Makris. \u003Ca href=\"https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.procir.2019.03.153\">On an evolutionary information system for personalized support to plant operators. \u003C\u002Fa> \u003Cem>52nd CIRP Conference on Manufacturing Systems (CMS), Ljubljana, Slovenia. \u003C\u002Fem>\u003C\u002Fa> June 2019.\n106) Michael Trotter, Timothy Wood and Jinho Hwang. \u003Ca href=\"http:\u002F\u002Ffaculty.cs.gwu.edu\u002Ftimwood\u002Fpapers\u002F19-ICAC-storm.pdf\">Forecasting a Storm: Divining Optimal Configurations using Genetic Algorithms and Supervised Learning. \u003C\u002Fa> \u003Cem>13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2019). \u003C\u002Fem>\u003C\u002Fa> June 2019.\n107) Krawczyk, Lukas & Bazzal, Mahmoud & Prasath Govindarajan, Ram & Wolff, Carsten. \u003Ca href=\"https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F334084554_An_analytical_approach_for_calculating_end-to-end_response_times_in_autonomous_driving_applications\">An analytical approach for calculating end-to-end response times in autonomous driving applications. \u003C\u002Fa> \u003Cem>10th International Workshop on Analysis Tools and Methodologies for Embedded and Real-time Systems (WATERS 2019). \u003C\u002Fem>\u003C\u002Fa> June 2019.\n108) Rodolfo Ayala Lopes, Thiago Macedo Gomes, and Alan Robert Resende de Freitas. \u003Ca href=\"http:\u002F\u002Fdelivery.acm.org\u002F10.1145\u002F3330000\u002F3326828\u002Fp1366-lopes.pdf?ip=84.114.111.7&id=3326828&acc=OPEN&key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E6D218144511F3437&__acm__=1563021092_5e8cda0c5ddddb14d4f5e9e3bd610a44\">A symbolic evolutionary algorithm software platform. \u003C\u002Fa> \u003Cem>Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '19). \u003C\u002Fem>\u003C\u002Fa> July 2019.\n109) Aleksandar Prokopec, Andrea Rosà, David Leopoldseder, Gilles Duboscq, Petr Tůma, Martin Studener, Lubomír Bulej, Yudi Zheng, Alex Villazón, Doug Simon, Thomas Würthinger, Walter Binder. \u003Ca href=\"https:\u002F\u002Frenaissance.dev\u002Fresources\u002Fdocs\u002Frenaissance-suite.pdf\">Renaissance: Benchmarking Suite for Parallel Applications on the JVM. \u003C\u002Fa> \u003Cem>PLDI ’19, Phoenix, AZ, USA. \u003C\u002Fem>\u003C\u002Fa> June 2019.\n110) Robert Höttger, Lukas Krawczyk, Burkhard Igel, Olaf Spinczyk. \u003Ca href=\"http:\u002F\u002F2019.rtas.org\u002Fwp-content\u002Fuploads\u002F2019\u002F04\u002FRTAS19_BP_proceedings.pdf#page=23\">Memory Mapping Analysis for Automotive Systems. \u003C\u002Fa> \u003Cem>Brief Presentations Proceedings (RTAS 2019). \u003C\u002Fem>\u003C\u002Fa> Apr. 2019.\n111) Al Akkad, M. A., & Gazimzyanov, F. F. \u003Ca href=\"http:\u002F\u002Fizdat.istu.ru\u002Findex.php\u002FISM\u002Farticle\u002Fview\u002F4317\">AUTOMATED SYSTEM FOR EVALUATING 2D-IMAGE COMPOSITIONAL CHARACTERISTICS: CONFIGURING THE MATHEMATICAL MODEL.\u003C\u002Fa> \u003Cem>Intellekt. Sist. Proizv., 17(1), 26-33. doi: 10.22213\u002F2410-9304-2019-1-26-33. \u003C\u002Fem>\u003C\u002Fa> Apr. 2019.\n112) Alcayde, A.; Baños, R.; Arrabal-Campos, F.M.; Montoya, F.G. \u003Ca href=\"https:\u002F\u002Fwww.mdpi.com\u002F1996-1073\u002F12\u002F7\u002F1270\">Optimization of the Contracted Electric Power by Means of Genetic Algorithms.\u003C\u002Fa> \u003Cem>Energies, Volume 12, Issue 7, \u003C\u002Fem>\u003C\u002Fa> Apr. 2019.\n113) Abdul Sahli Fakharudin, Norazwina Zainol, Zulsyazwan Ahmad Khushairi. \u003Ca href=\"https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3323716.3323737\">Modelling and Optimisation of Oil Palm Trunk Core Biodelignification using Neural Network and Genetic Algorithm.\u003C\u002Fa> \u003Cem>IEEA '19: Proceedings of the 8th International Conference on Informatics, Environment, Energy and Applications; Pages 155–158, \u003C\u002Fem>\u003C\u002Fa> Mar. 2019.\n114) Aleksandar Prokopec, Andrea Rosà, David Leopoldseder, Gilles Duboscq, Petr Tůma, Martin Studener, Lubomír Bulej, Yudi Zheng, Alex Villazón, Doug Simon, Thomas Wuerthinger, Walter Binder. \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.10267.pdf\">On Evaluating the Renaissance Benchmarking Suite: Variety, Performance, and Complexity.\u003C\u002Fa> \u003Cem>Cornell University: Programming Languages, \u003C\u002Fem>\u003C\u002Fa> Mar. 2019.\n115) S. Appel, W. Geithner, S. Reimann, M Sapinski, R. Singh, D. M. Vilsmeier \u003Ca href=\"https:\u002F\u002Fwww.researchgate.net\u002Fprofile\u002FSabrina_Appel\u002Fpublication\u002F330934110_OPTIMIZATION_OF_HEAVY-ION_SYNCHROTRONS_USING_NATURE-INSPIRED_ALGORITHMS_AND_MACHINE_LEARNING\u002Flinks\u002F5c5c425b299bf1d14cb33546\u002FOPTIMIZATION-OF-HEAVY-ION-SYNCHROTRONS-USING-NATURE-INSPIRED-ALGORITHMS-AND-MACHINE-LEARNING.pdf\">OPTIMIZATION OF HEAVY-ION SYNCHROTRONS USINGNATURE-INSPIRED ALGORITHMS AND MACHINE LEARNING.\u003C\u002Fa>\u003Cem>\u003Ca href=\"https:\u002F\u002Fbt.pa.msu.edu\u002FICAP18\u002Findex.html\">13th Int. Computational Accelerator Physics Conf.\u003C\u002Fa>, \u003C\u002Fem>\u003C\u002Fa> Feb. 2019.\n116) Saad, Christian, Bernhard Bauer, Ulrich R Mansmann, and Jian Li. \u003Ca href=\"https:\u002F\u002Fjournals.sagepub.com\u002Fdoi\u002F10.1177\u002F1177932218818458\">AutoAnalyze in Systems Biology.\u003C\u002Fa> \u003Cem>Bioinformatics and Biology Insights, \u003C\u002Fem>\u003C\u002Fa> Jan. 2019.\n117) Gandeva Bayu Satrya, Soo Young Shin. \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fpdf\u002F1812.01201.pdf\">Evolutionary Computing Approach to Optimize Superframe Scheduling on Industrial Wireless Sensor Networks.\u003C\u002Fa> \u003Cem>Cornell University, \u003C\u002Fem>\u003C\u002Fa> Dec. 2018.\n118) H.R. Maier, S. Razavi, Z. Kapelan, L.S. Matott, J. 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[Die Evolution im Algorithmus - Teil 2: Multikriterielle Optimierung und Architekturerkennung.](http:\u002F\u002Fwww.buschmais.de\u002Fwp-content\u002Fuploads\u002F2018\u002F06\u002FDie-Evolution-im-Algorithmus_Teil2_JS_03_18.pdf) \u003Ca href=\"https:\u002F\u002Fwww.sigs-datacom.de\u002Fdigital\u002Fjavaspektrum\u002F\">\u003Cem>JavaSPEKTRUM 03\u002F2018, pp 66–69, \u003C\u002Fem>\u003C\u002Fa> May 2018.\n126) W. Geithner, Z. Andelkovic, S. Appel, O. Geithner, F. Herfurth, S. Reimann, G. Vorobjev, F. Wilhelmstötter. [Genetic Algorithms for Machine Optimization in the Fair Control System Environment.](http:\u002F\u002Faccelconf.web.cern.ch\u002FAccelConf\u002Fipac2018\u002Fpapers\u002Fthpml028.pdf)\u003Cem> [The 9th International Particle Accelerator Conference (IPAC'18)](https:\u002F\u002Fipac18.org\u002Fwelcome\u002F), \u003C\u002Fem>\u003C\u002Fa> May 2018.\n127) Stephan Pirnbaum. [Die Evolution im Algorithmus - Teil 1: Grundlagen.](http:\u002F\u002Fwww.buschmais.de\u002Fwp-content\u002Fuploads\u002F2018\u002F02\u002FDie-Evolution-im-Algorithmus_JS_01_18.pdf) \u003Ca href=\"https:\u002F\u002Fwww.sigs-datacom.de\u002Fdigital\u002Fjavaspektrum\u002F\">\u003Cem>JavaSPEKTRUM 01\u002F2018, pp 64–68, \u003C\u002Fem>\u003C\u002Fa> Jan. 2018.\n128) Alexander Felfernig, Rouven Walter, José A. Galindo, David Benavides, Seda Polat Erdeniz, Müslüm Atas, Stefan Reiterer. \u003Ca href=\"https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10844-017-0492-1\">Anytime diagnosis for reconfiguration. \u003C\u002Fa> \u003Cem>Journal of Intelligent Information Systems, pp 1–22, \u003C\u002Fem> Jan. 2018.\n129) Bruce A. Johnson. \u003Ca href=\"https:\u002F\u002Flink.springer.com\u002Fprotocol\u002F10.1007\u002F978-1-4939-7386-6_13\">From Raw Data to Protein Backbone Chemical Shifts Using NMRFx Processing and NMRViewJ Analysis. \u003C\u002Fa> \u003Cem>Protein NMR: Methods and Protocols, pp. 257--310, Springer New York, \u003C\u002Fem> Nov. 2017.\n130) Cuadra P., Krawczyk L., Höttger R., Heisig P., Wolff C. \u003Ca href=\"https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-3-319-67642-5_30\">Automated Scheduling for Tightly-Coupled Embedded Multi-core Systems Using Hybrid Genetic Algorithms. \u003C\u002Fa> \u003Cem>Information and Software Technologies: 23rd International Conference, ICIST 2017, Druskininkai, Lithuania.\u003C\u002Fem> Communications in Computer and Information Science, vol 756. 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Louise Maffitt. \u003Ca href=\"http:\u002F\u002Fprod.sandia.gov\u002Ftechlib\u002Faccess-control.cgi\u002F2012\u002F121117.pdf\"> \u003Cem>Complex Adaptive Systems of Systems Engineering Environment Version 1.0.  \u003C\u002Fem>\u003C\u002Fa> \u003Ca href=\"http:\u002F\u002Fwww.sandia.gov\u002FCasosEngineering\u002F\">SAND REPORT\u003C\u002Fa>, Feb. 2012.\n\u003C\u002Fdetails>\n\n## 发行说明\n\n### [9.0.0](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Freleases\u002Ftag\u002Fv9.0.0)\n\n#### 改进\n\n* 更新至 Java 25，并针对新版本 Java 优化代码。\n* [#917](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F917)：为 `RandomRegistry` 类引入 `ScopedValue`。\n* [#940](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F940)：移除已弃用的 API。\n* [#955](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F955)：使 `IntStream` 计数更加健壮。\n\n_[所有发行说明](RELEASE_NOTES.md)_\n\n## 许可证\n\n本库采用 [Apache License, Version 2.0](http:\u002F\u002Fwww.apache.org\u002Flicenses\u002FLICENSE-2.0.html) 许可证授权。\n\n\tCopyright 2007-2026 Franz Wilhelmstötter\n\n\t根据 Apache License, Version 2.0（“许可证”）授权；\n\t除非符合许可证规定，否则不得使用本文件。\n\t您可以在以下地址获取许可证副本：\n\n\thttp:\u002F\u002Fwww.apache.org\u002Flicenses\u002FLICENSE-2.0\n\n\t除非适用法律要求或双方另有约定，否则软件按“原样”分发，\n\t不提供任何形式的保证或条件。有关具体语言的权限及限制，\n\t请参阅许可证文本。\n\n\n## 使用的软件\n\n\n\u003Ca href=\"https:\u002F\u002Fwww.jetbrains.com\u002Fidea\u002F\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjenetics_jenetics_readme_1710e9ac5bf0.png\" alt=\"IntelliJ\" height=\"100\"\u002F>\u003C\u002Fa>\n\n\u003Ca href=\"https:\u002F\u002Fwww.syntevo.com\u002Fsmartgit\u002F\">\u003Cimg src=\"https:\u002F\u002Fwww.syntevo.com\u002Fassets\u002Fimages\u002Flogos\u002Fsmartgit-8c1aa1e2.svg\" alt=\"SmartGit\" height=\"100\"\u002F>\u003C\u002Fa>","# Jenetics 快速上手指南\n\nJenetics 是一个用现代 Java 编写的遗传算法（GA）、进化算法、语法进化和多目标优化库。它通过清晰的类结构（如 `Gene`、`Chromosome`、`Genotype`）分离算法概念，并利用 Java Stream API 风格的 `EvolutionStream` 来执行进化步骤。\n\n## 环境准备\n\n*   **操作系统**：Linux, macOS, Windows\n*   **JDK 版本**：**Java 25** 或更高版本（编译和运行必需）\n*   **构建工具**：Gradle（用于源码构建），或 Maven\u002FGradle（用于项目依赖管理）\n\n> **注意**：请确保您的 `JAVA_HOME` 环境变量已正确指向 Java 25+。\n\n## 安装步骤\n\n### 方式一：通过 Maven 依赖（推荐）\n\n如果您使用 Maven 构建项目，请在 `pom.xml` 中添加以下依赖：\n\n```xml\n\u003Cdependency>\n    \u003CgroupId>io.jenetics\u003C\u002FgroupId>\n    \u003CartifactId>jenetics\u003C\u002FartifactId>\n    \u003Cversion>9.0.0\u003C\u002Fversion> \u003C!-- 请使用最新稳定版本 -->\n\u003C\u002Fdependency>\n```\n\n若需使用扩展功能（如多目标优化、语法进化），可额外添加：\n\n```xml\n\u003Cdependency>\n    \u003CgroupId>io.jenetics\u003C\u002FgroupId>\n    \u003CartifactId>jenetics.ext\u003C\u002FartifactId>\n    \u003Cversion>9.0.0\u003C\u002Fversion>\n\u003C\u002Fdependency>\n```\n\n### 方式二：通过 Gradle 依赖\n\n在 `build.gradle` 中添加：\n\n```groovy\ndependencies {\n    implementation 'io.jenetics:jenetics:9.0.0'\n    \u002F\u002F implementation 'io.jenetics:jenetics.ext:9.0.0' \u002F\u002F 可选扩展模块\n}\n```\n\n### 方式三：源码编译\n\n如果您希望从源码构建：\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics.git \u003Cbuilddir>\ncd \u003Cbuilddir>\n.\u002Fgradlew jar\n```\n\n编译后的 JAR 文件位于 `\u003Cbuilddir>\u002F\u003Cmodule-dir>\u002Fbuild\u002Flibs` 目录下。\n\n## 基本使用\n\n以下是一个经典的\"Hello World\"示例：通过遗传算法最大化二进制染色体中\"1\"的数量。\n\n### 代码示例\n\n```java\nimport io.jenetics.BitChromosome;\nimport io.jenetics.BitGene;\nimport io.jenetics.Genotype;\nimport io.jenetics.engine.Engine;\nimport io.jenetics.engine.EvolutionResult;\nimport io.jenetics.util.Factory;\n\npublic class HelloWorld {\n    \u002F\u002F 2. 定义适应度函数 (计算 1 的个数)\n    private static Integer eval(Genotype\u003CBitGene> gt) {\n        return gt.chromosome()\n            .as(BitChromosome.class)\n            .bitCount();\n    }\n\n    public static void main(String[] args) {\n        \u002F\u002F 1. 定义基因型工厂 (创建一个长度为 10 的二进制染色体)\n        Factory\u003CGenotype\u003CBitGene>> gtf =\n            Genotype.of(BitChromosome.of(10, 0.5));\n\n        \u002F\u002F 3. 创建进化引擎\n        Engine\u003CBitGene, Integer> engine = Engine\n            .builder(HelloWorld::eval, gtf)\n            .build();\n\n        \u002F\u002F 4. 启动进化流并收集结果 (限制为 100 代)\n        Genotype\u003CBitGene> result = engine.stream()\n            .limit(100)\n            .collect(EvolutionResult.toBestGenotype());\n\n        System.out.println(\"Hello World:\\n\" + result);\n    }\n}\n```\n\n### 核心步骤解析\n\n1.  **定义基因型 (`Genotype`)**：将问题域映射为基因表示。本例中使用了 `BitChromosome`（二进制染色体）。\n2.  **定义适应度函数**：编写一个方法评估个体的优劣。本例中直接调用 `bitCount()` 统计 1 的个数作为得分。\n3.  **构建引擎 (`Engine`)**：配置进化环境。`Engine.builder` 需要传入适应度函数和基因型工厂。\n4.  **执行进化 (`EvolutionStream`)**：\n    *   调用 `engine.stream()` 获取进化流。\n    *   利用 Java Stream API 控制流程（如 `.limit(100)` 限制进化代数）。\n    *   使用 `EvolutionResult.toBestGenotype()` 收集最佳结果。\n\nJenetics 的设计使其能无缝集成到现有的 Java Stream 工作流中，您可以像处理普通数据流一样处理进化过程（例如使用 `filter`, `peek`, `parallel` 等操作）。","某金融科技团队正在开发高频交易策略，需要从数千个技术指标参数组合中自动寻找收益最大化的最优配置。\n\n### 没有 jenetics 时\n- 开发人员需手动编写复杂的遗传算法底层逻辑，包括基因编码、交叉变异算子及种群管理，代码冗余且极易出错。\n- 面对多目标优化（如同时最大化收益并最小化回撤）时，缺乏现成框架支持，只能硬编码启发式规则，导致策略收敛缓慢。\n- 难以利用 Java Stream API 进行并行计算优化，每次全量回测耗时数小时，严重拖慢迭代节奏。\n- 调整适应度函数往往需要重构核心演化流程，耦合度高，无法灵活应对市场风格切换。\n\n### 使用 jenetics 后\n- 直接调用 Jenetics 预定义的 `Gene`、`Chromosome` 和 `Engine` 组件，几行代码即可构建标准的演化流，将核心算法开发时间从数周缩短至几天。\n- 利用 `jenetics.ext` 模块原生支持的多目标优化算法（MOEA），轻松平衡收益与风险，无需自行设计复杂的权重评分机制。\n- 基于 `EvolutionStream` 无缝集成 Java Stream API，天然支持并行处理，将大规模参数空间的搜索效率提升数倍。\n- 适应度函数与演化逻辑完全解耦，修改策略评估标准只需替换函数实现，无需触碰底层演化引擎，极大提升了实验灵活性。\n\nJenetics 通过标准化的演化流设计和模块化架构，让复杂的全局优化问题变得像编写普通 Java 流处理一样简单高效。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjenetics_jenetics_f83fa753.png","Franz Wilhelmstötter","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fjenetics_1defc305.jpg","Java, Kotlin, Messaging, Web services,  Distributed systems, API design, Architectures, Partial stack developer",null,"Vienna, Wien, Austria","development@jenetics.io","jeneticsga","https:\u002F\u002Fjenetics.io","https:\u002F\u002Fgithub.com\u002Fjenetics",[82,86,90,94,98,102,106,110,113,116],{"name":83,"color":84,"percentage":85},"Java","#b07219",96.6,{"name":87,"color":88,"percentage":89},"CSS","#663399",0.9,{"name":91,"color":92,"percentage":93},"TeX","#3D6117",0.8,{"name":95,"color":96,"percentage":97},"Gnuplot","#f0a9f0",0.6,{"name":99,"color":100,"percentage":101},"Kotlin","#A97BFF",0.5,{"name":103,"color":104,"percentage":105},"Shell","#89e051",0.2,{"name":107,"color":108,"percentage":109},"JavaScript","#f1e05a",0.1,{"name":111,"color":112,"percentage":109},"C++","#f34b7d",{"name":114,"color":115,"percentage":109},"Python","#3572A5",{"name":117,"color":118,"percentage":109},"ANTLR","#9DC3FF",894,161,"2026-04-17T10:00:08","Apache-2.0",4,"Linux, macOS, Windows","未说明",{"notes":127,"python":128,"dependencies":129},"该工具是基于 Java 的遗传算法库，非 Python 项目。编译和运行至少需要 Java 25 版本。使用 Gradle 作为构建系统。无需 GPU 加速，主要依赖 CPU 进行进化计算。","不适用",[130,131],"Java 25+","Gradle (构建工具)",[14],[134,135,136,137,138,139,140,141,142,143,144,145,146],"evolutionary-algorithms","genetic-algorithm","artificial-intelligence","optimization","evolutionary-strategy","parallel-algorithm","metaheuristics","genetic-programming","machine-learning","java","multiobjective-optimization","grammatical-evolution","java25","2026-03-27T02:49:30.150509","2026-04-18T17:05:32.043218",[150,155,160,165,170,174],{"id":151,"question_zh":152,"answer_zh":153,"source_url":154},40442,"Jenetics 是否支持基于语法的进化（Grammar-based Evolution）？","是的，该功能已在 `io.jenetics.incubator` 模块中实现，并合并到了 r7.0.0 分支。用户可以通过 BNF 字符串创建上下文无关文法，并使用 `Sentence.codec` 将整数数组（密码子）映射为程序树。示例代码如下：\n\u002F\u002F 从 BNF 字符串创建上下文无关文法\nprivate static final Cfg CFG = Bnf.parse(\"\"\"\n    \u003Cexpr> ::= (\u003Cexpr>\u003Cop>\u003Cexpr>) | \u003Cvar>\n    \u003Cop> ::= + | - | *\n    \u003Cvar> ::= x | 1 | 2 | 3 | 4\n    \"\"\"\n);\n\u002F\u002F 创建从 int[] 数组生成程序树的 Codec\nprivate static final Codec\u003CTree\u003C? extends Op\u003CDouble>, ?>, IntegerGene> CODEC =\n    Sentence\n        .codec(CFG, IntRange.of(0, 256), IntRange.of(30), 500)\n        .map(Sentence::toString)\n        .map(e -> e.isEmpty() ? null : MathExpr.parseTree(e));","https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F767",{"id":156,"question_zh":157,"answer_zh":158,"source_url":159},40443,"如何优化适应度评估执行以支持批量计算（例如算法交易回测中的分块处理）？","Jenetics 已支持自定义适应度评估执行机制。对于需要成批处理基因型以提高效率的场景（如减少数据遍历次数），库允许用户覆盖评估逻辑或提供接收整个种群而非单个基因型的适应度函数。该功能已合并到 r4.2.0 分支。这使得用户可以按 CPU 核心数将种群分块，从而在多线程环境中复用缓存的指标和信号，显著降低 I\u002FO 和计算开销。","https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F325",{"id":161,"question_zh":162,"answer_zh":163,"source_url":164},40444,"如果等位基因包含多个不同范围的数值，是否需要自定义基因类？","当问题的等位基因由多个具有不同取值范围的数值组成时（例如两个 0-9 的整数和一个 0-5 的整数），通常建议创建自定义基因类来封装这些值。如果在最小化优化（Optimize.MINIMUM）模式下遇到 `IllegalArgumentException: bound must be positive` 错误，这通常与交叉算子（如 SinglePointCrossover）在处理自定义染色体结构时的边界检查有关。确保自定义基因和染色体正确实现了必要的接口方法，并且随机数生成的范围参数均为正数。","https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F330",{"id":166,"question_zh":167,"answer_zh":168,"source_url":169},40445,"如何在 Jenetics 中创建具有不同约束条件的双基因染色体？","虽然库本身没有直接的“双基因”预设类型，但可以通过组合不同的基因类型或自定义染色体来实现具有不同约束条件的基因结构。用户通常需要定义一个包含多个基因的染色体，每个基因可以有不同的范围或约束（例如使用 `DoubleGene` 的不同 `DoubleRange`）。如果遇到具体实现困难，建议创建新的 Issue 并提供详细的代码片段和错误堆栈，以便维护者提供针对性的帮助。","https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F287",{"id":171,"question_zh":172,"answer_zh":173,"source_url":169},40446,"如何在适应度函数中实现延迟执行或异步数据读取？","Jenetics 的适应度函数默认是同步执行的。如果需要在解码基因型后等待特定时间段或从缓冲区异步读取数据，直接在适应度函数中使用 `Executor` 可能会导致执行停止或死锁。推荐的解决方案是在适应度函数外部处理异步逻辑，或者确保异步任务在适应度评估超时前完成并返回结果。如果必须等待，应考虑使用阻塞式调用确保数据就绪后再计算适应度，而不是依赖可能导致线程挂起的异步回调机制。",{"id":175,"question_zh":176,"answer_zh":177,"source_url":159},40447,"是否有差分进化（Differential Evolution）交叉算子的实现示例？","是的，社区用户已经实现了 `DifferentialEvolutionCrossover` 并分享了用法。该算子可用于投资组合优化等场景。使用时需定义适应度函数，将基因型转换为双精度向量进行计算。维护者鼓励用户贡献额外的 alterer 实现，并希望看到更多关于其属性、应用领域及真实世界示例（如 `Engine.Evaluator` 的实现草图）的文档，以便集成到官方文档中。",[179,184,189,194,199,204,209,214,219,224,229,234,239,244,249,254,259,264,269,274],{"id":180,"version":181,"summary_zh":182,"released_at":183},323847,"v9.0.0","### 改进\n\n* 更新 Java 25 并针对新版本的 Java 优化代码。\n* [#917](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F917)：为 `RandomRegistry` 类引入 `ScopedValue`。\n* [#940](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F940)：移除已弃用的 API。\n* [#955](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F955)：使 `IntStream` 计数更加健壮。","2026-01-16T20:36:47",{"id":185,"version":186,"summary_zh":187,"released_at":188},323848,"v8.3.0","### 改进\n\n* [#933](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F933)：弃用 `RandomAdapter`，准备移除。\n* [#935](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F935)：使用 Java 24\u002F25 编译并测试 Jenetics。\n* [#938](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F938)：将 `Range` 类转换为记录类。\n* [#943](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F943)：移除 `org.apache.commons:commons-math3` 测试依赖。\n* [#946](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F946)：创建 `io.jenetics.distassert` 模块，供统计遗传算法测试使用。\n* [#948](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F948)：改进 `GaussianMutator` 的实现。\n* [#951](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F951)：改进 `RandomRegistry` 的测试。\n* \n### Bug 修复\n\n* [#936](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F936)：修复 `assemblePkg` 任务。\n* [#941](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F941)：在升级 [TestNG](https:\u002F\u002Fgithub.com\u002Ftestng-team\u002Ftestng) 后修复统计测试。\n* [#952](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F952)：修复工件发布问题。\n* [#955](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fpull\u002F945)：改进随机通用选择算子。","2025-09-22T11:47:06",{"id":190,"version":191,"summary_zh":192,"released_at":193},323849,"v8.2.0","### 改进\n\n* [#889](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F889)：允许为 _语法进化_ 的 `Cfg` 元素添加注解。\n```java\nfinal var cfg2 = Cfg.\u003CString>builder()\n    .R(\"expr\", rule -> rule\n        .N(\"num\", \"annotation 1\")\n        .N(\"var\", \"annotation 2\")\n        .E(exp -> exp\n            .T(\"(\")\n            .N(\"expr\").N(\"op\", 4).N(\"expr\")\n            .T(\")\")))\n    .R(\"op\", rule -> rule.T(\"+\").T(\"-\").T(\"*\").T(\"\u002F\"))\n    .R(\"var\", rule -> rule.T(\"x\").T(\"y\"))\n    .R(\"num\", rule -> rule\n        .T(\"0\").T(\"1\").T(\"2\").T(\"3\").T(\"4\")\n        .T(\"5\").T(\"6\").T(\"7\").T(\"8\").T(\"9\")\n    )\n    .build();\n```\n* [#915](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F915)：移除对 `java.security.AccessController` 的使用。\n* [#921](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F921)：移除 `equals` 方法中 `object == this` 的“优化”。\n* [#923](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F923)：提升 `CsvSupport` 的解析性能。\n* [#925](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F925)：_孵化中_：实现统计假设检验器。引擎类的统计测试已稳定，可以按以下方式编写。\n```java\nfinal var observation = new RunnableObservation(\n    Sampling.repeat(200_000, samples ->\n        samples.add(DoubleGene.of(0, 20).doubleValue())\n    ),\n    Partition.of(0, 20, 20)\n);\nnew StableRandomExecutor(seed).execute(observation);\n\nassertThatObservation(observation).isUniform();\n```\n\n### Bug 修复\n\n* [#914](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F#914)：修复 `Samplers.linear(double)` 工厂方法。","2025-04-07T19:02:15",{"id":195,"version":196,"summary_zh":197,"released_at":198},323850,"v8.1.0","### 改进\n\n* [#822](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F822)：改进用于生成合并 Javadoc 的构建脚本。\n* [#898](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F898)：添加从 CSV 文件或字符串中读取数据的支持。这简化了回归问题的代码。\n```java\nstatic List\u003CSample\u003CDouble>> parseDoubles(final CharSequence csv) {\n\treturn CsvSupport.parseDoubles(csv).stream()\n\t\t.map(Sample::ofDouble)\n\t\t.toList();\n}\n```\n* [#904](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F904)：升级到 Gradle 8.10，并清理构建脚本。\n* [#907](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F907)：在用户手册中新增一章，介绍优化策略：《实用 Jenetics》。\n* [#909](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F909)：用于转换原始类型数组的辅助方法。\n```java\nfinal Codec\u003Cint[], DoubleGene> codec = Codecs\n    .ofVector(DoubleRange.of(0, 100), 100)\n    .map(Conversions::doubleToIntArray);\n```\n\n### Bug 修复\n\n* [#419](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F#419)：修复不稳定的统计测试。","2024-09-14T21:21:54",{"id":200,"version":201,"summary_zh":202,"released_at":203},323851,"v8.0.0","### 改进\n\n* 构建和使用该库时采用 Java 21。\n* [#878](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F878)：允许使用虚拟线程评估适应度函数。必须在创建 `Engine` 时启用（见下方代码片段），同时保留了原有行为。\n```java\nfinal Engine\u003CDoubleGene, Double> engine = Engine.builder(ff)\n\t.fitnessExecutor(BatchExecutor.ofVirtualThreads())\n\t.build();\n```\n* [#880](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F880)：将 Javadoc 中的代码示例替换为 [JEP 413](https:\u002F\u002Fopenjdk.org\u002Fjeps\u002F413) 格式。\n* [#886](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F886)：改进 `CharStore` 的排序。\n* [#894](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F894)：新增遗传算子：`ShiftMutator`、`ShuffleMutator` 和 `UniformOrderBasedCrossover`。\n* [#895](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F895)：改进默认 `RandomGenerator` 的选择机制。所使用的随机数生成器按以下顺序选取：\n\t1) 检查是否设置了 `io.jenetics.util.defaultRandomGenerator` 启动参数。如果已设置，则使用该生成器。\n\t2) 检查是否可用 `L64X256MixRandom` 生成器。如果可用，则使用该生成器。\n\t3) 根据 `RandomGeneratorFactory.stateBits()` 值，选择“最佳”的可用随机数生成器。\n\t4) 如果无法找到“最佳”生成器，则使用 `Random` 生成器。该生成器在所有平台上均保证可用。","2024-03-22T19:25:54",{"id":205,"version":206,"summary_zh":207,"released_at":208},323852,"v7.2.0","### 改进\n\n* [#862](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F862)：添加一个方法，允许基于给定的基因型创建切片（染色体）视图。\n* [#866](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F866)：允许指定库使用的默认 `RandomGenerator`。\n``` \njava -Dio.jenetics.util.defaultRandomGenerator=L64X1024MixRandom\\\n     -cp jenetics-@__version__@.jar:app.jar\\\n         com.foo.bar.MyJeneticsAppjava \n```\n\n* [#872](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F872)：改进 `io.jenetics.prog` 模块中部分参数类型的泛型类型参数。\n* [#876](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F876)：修复 Java 21 下的编译器警告。\n\n### Bug 修复\n\n* [#865](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F865)、[#867](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F867)：修正文档中的拼写错误。\n* [#868](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F868)：修复执行脚本 `.\u002Fjrun.cmd`。","2023-08-30T18:33:18",{"id":210,"version":211,"summary_zh":212,"released_at":213},323853,"v7.1.3","### 改进\r\n\r\n* [#857](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F857)：使该库能够使用 Java 20 编译。","2023-04-21T18:14:00",{"id":215,"version":216,"summary_zh":217,"released_at":218},323854,"v7.1.2","### 改进\n\n* [#853](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F853)：改进 `Codecs::ofSubSet::encode` 方法的错误信息。","2023-03-06T18:46:15",{"id":220,"version":221,"summary_zh":222,"released_at":223},323855,"v7.1.1","### Bug 修复\n\n* [#842](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F842)：当染色体长度小于等于 8 时，`BitChromosone::bitCount` 方法返回的结果不正确。","2022-10-16T17:46:22",{"id":225,"version":226,"summary_zh":227,"released_at":228},323856,"v7.1.0","### 改进\n\n* [#813](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F813)：重新实现 `MathExpr` 类。替换临时性的解析实现。\n* [#815](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F815)：实现语法进化算法。\n* [#820](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F820)：为 `BitChromosome` 添加额外的方法：`and`、`or`、`xor`、`not`、`shiftRight`、`shiftLeft`。\n* [#833](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F833)：实现 `Tree::reduce` 函数。允许编写如下代码：\n```java\nfinal Tree\u003CString, ?> formula = TreeNode.parse(\n    \"add(sub(6, div(230, 10)), mul(5, 6))\",\n    String::trim\n);\nfinal double result = formula.reduce(new Double[0], (op, args) ->\n    switch (op) {\n        case \"add\" -> args[0] + args[1];\n        case \"sub\" -> args[0] - args[1];\n        case \"mul\" -> args[0] * args[1];\n        case \"div\" -> args[0] \u002F args[1];\n        default -> Double.parseDouble(op);\n    }\n);\n```\n\n### Bug 修复\n\n* [#831](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F831)：解析括号树时出现错误。\n* [#836](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F836)：修复 `BitChromosome`（测试）。","2022-06-15T18:12:07",{"id":230,"version":231,"summary_zh":232,"released_at":233},323857,"v7.0.0","### Improvements\r\n\r\n* [#632](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F632): Convert data classes to `records`.\r\n* [#696](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F693): Convert libraries to JPMS modules.\r\n* [#715](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F715): Improve `BitChromosome`.\r\n* [#762](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F762): **Breaking change** Update to Java 17.\r\n* [#767](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F767): **Incubator** - Grammar-based evolution.\r\n* [#773](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F773): **Incubator** - Simplify and unify parsing code for `MathExpr` class.\r\n* [#785](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F785): Using `RandomGenerator` instead of `Random` class.\r\n* [#787](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F787): **Breaking change** - Change upper limit of `Integer`\u002F`LongeGenes` from _inclusively_ to _exclusively_.\r\n* [#789](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F789): Make `AbstractChromosome` non-`Serializable`.\r\n* [#796](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F796): Use `InstantSource` instead of `Clock` for measuring evolution durations.\r\n* [#798](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F798): Performance improve of _subset_ creation method.\r\n* [#801](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F801): Introduce `Self` interface.\r\n* [#816](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F816): Add Sudoku example (by [alex-cornejo](https:\u002F\u002Fgithub.com\u002Falex-cornejo)).\r\n\r\n### Bugs\r\n\r\n* [#791](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F791): Fix possible overflow in Integer\u002FLongGene mean method.\r\n* [#794](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F794): Fix possible underflow in DoubleGene mean method.\r\n* [#803](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F803): Bug checking Sample arity in class SampleList.","2022-02-21T18:15:42",{"id":235,"version":236,"summary_zh":237,"released_at":238},323858,"v6.3.0","### Improvements\r\n\r\n* [#763](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F763): `ProxySorter` is now able to sort array slices.\r\n* [#768](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F768): Implement `Ordered` class. Currently, it is required that the return value of the fitness function to be `Comparable`. But sometimes you might want to change the order of a given type or add some order to a type. The `Ordered` class makes this possible.","2021-08-28T16:21:30",{"id":240,"version":241,"summary_zh":242,"released_at":243},323859,"v6.2.0","### Improvements\r\n\r\n* [#754](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F754): Make `Optimize.best` method `null` friendly.\r\n  \r\n### Bugs\r\n\r\n* [#742](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F742): Fix compile error with Java 15.\r\n* [#746](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F746): `Const\u003CDouble>` equals doesn't conform with `Double.compare`.\r\n* [#748](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F748): Fix broken formulas in Javadoc.\r\n* [#752](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F752): `StreamPublisher` doesn't close underlying `Stream` on close.","2021-02-09T13:12:50",{"id":245,"version":246,"summary_zh":247,"released_at":248},323860,"v6.1.0","### Improvements\r\n\r\n* [#323](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F323): Fix leaky abstraction of `CompositeCodec`. \r\n* [#434](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F434): Rewrite build scripts using Kotlin.\r\n* [#695](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F695): Simplify MOEA for continious optimization.\r\n* [#704](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F704): Add `FlatTreeNode.ofTree` factory method, for cleaner `Tree` API.\r\n* [#706](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F706): The `Constraint` is now part of the `Problem` interface. If defined, it will automatically be part of the created `Engine`.\r\n```java\r\ndefault Optional\u003CConstraint\u003CG, C>> constraint() {\r\n    return Optional.empty();\r\n}\r\n```\r\n* [#708](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F708): Additional `Chromosome.map(Function)` methods. This allows a more efficient mapping of chromosomes.\r\n* [#731](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F731): Improve creation of _constrained_ individuals, as defined in the `Constraint` interface.\r\n* [#739](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F736): Add `jenetics.incubator` module. This module will contain classes which might be part of one of the main module.\r\n\r\n### Bugs\r\n\r\n* [#700](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F700): `GaussianMutator` violates the `DoubleGene`'s upper bound.\r\n* [#707](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F707): Fix conversion of `BitChromosome` \u003C-> `BitSet`.","2020-09-15T19:52:41",{"id":250,"version":251,"summary_zh":252,"released_at":253},323861,"v6.0.1","### Bugs\r\n\r\n* [#701](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F701): Invalid `DoubleGene.isValid` method.\r\n* [#713](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F713): Fix numeric instability of `RouletteWheleSelector`class.\r\n* [#718](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F718): `IntermediateCrossover` is not terminating for invalid genes.","2020-06-23T19:17:03",{"id":255,"version":256,"summary_zh":257,"released_at":258},323862,"v6.0.0","### Improvements\r\n\r\n* [#403](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F403): Converting library to Java 11.\r\n* [#581](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F581): Minimize the required _evaluation_ calls per generation.\r\n* [#587](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F587): Fix Javadoc for Java 11.\r\n* [#590](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F590): Improve serialization of `Seq` implementations.\r\n* [#591](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F591): Remove deprecated classes and methods.\r\n* [#606](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F606): Improve serialization of `*Range` classes.\r\n* [#630](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F630): Fix inconsistency in `Codec.of` factory methods.\r\n* [#659](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F659): Additional factory methods for `VecFactory` interface in the `moea` package.\r\n* [#661](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F661): Allow the re-evaluation of the population fitness value\r\n* [#665](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F665): Implement `CombineAlterer`, which is a generalization of th `MeanAlterer` class.\r\n* [#669](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F669): Regression analysis with dynamically changing sample points.\r\n```java\r\nfinal var scheduler = Executors.newScheduledThreadPool(1);\r\nfinal var nullifier = new FitnessNullifier\u003CProgramGene\u003CDouble>, Double>();\r\nfinal var sampling = new SampleBuffer\u003CDouble>(100);\r\nscheduler.scheduleWithFixedDelay(\r\n    () -> {\r\n        \u002F\u002F Adding a new sample point every second to the ring buffer.\r\n        sampling.add(nextSamplePoint());\r\n        \u002F\u002F Force re-evaluation of populations fitness values.\r\n        nullifier.nullifyFitness();\r\n    },\r\n    1, 1, TimeUnit.SECONDS\r\n);\r\n\r\nfinal Codec\u003CTree\u003COp\u003CDouble>, ?>, ProgramGene\u003CDouble>> codec =\r\n    Regression.codecOf(OPS, TMS, 5, t -> t.gene().size() \u003C 30);\r\n\r\nfinal Regression\u003CDouble> regression = Regression.of(\r\n    codec,\r\n    Error.of(LossFunction::mse),\r\n    sampling\r\n);\r\n\r\nfinal Engine\u003CProgramGene\u003CDouble>, Double> engine = Engine\r\n    .builder(regression)\r\n    .interceptor(nullifier)\r\n    .build();\r\n\r\nengine.stream()\r\n    .flatMap(Streams.toIntervalMax(Duration.ofSeconds(30)))\r\n    .map(program -> program.bestPhenotype()\r\n        .genotype().gene()\r\n        .toParenthesesString())\r\n    \u002F\u002F Printing the best program found so far every 30 seconds.\r\n    .forEach(System.out::println);\r\n```\r\n* [#671](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F671): Adding helper methods in `Streams` class, which allows to emit the best evolution result of every _n_ generation.\r\n```java\r\nfinal ISeq\u003CInteger> values = IntStream.range(0, streamSize).boxed()\r\n    .flatMap(Streams.toIntervalMax(sliceSize))\r\n    .collect(ISeq.toISeq());\r\n``` \r\n* [#672](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F672): Introduce the `StreamPublisher` class, which allows to use a _normal_ Java Stream in a _reactive_ way.\r\n```java\r\nfinal var publisher = new StreamPublisher\u003CEvolutionResult\u003CIntegerGene, Integer>>();\r\ntry (publisher) {\r\n    final var stream= engine.stream();\r\n    publisher.subscribe(new Subscriber\u003C>() { ... });\r\n    publisher.attach(stream);\r\n    ...\r\n}\r\n```\r\n* [#679](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F679): Additional constructor for the `TournamentSelector`, which allows to define own `Phenotype` comparator.\r\n* [#685](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F685): Add `Engine.Setup` interface, which allows combining different dependent engine configurations.\r\n* [#687](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F687): Add engine setup for (μ,λ)- and (μ+λ)-Evolution Strategy.\r\n\r\n### Bugs\r\n\r\n* [#663](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F663): `PartialAlterer` uses fitness of unaltered phenotype.\r\n* [#667](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F667): Fix `Concurrency.close()` method.","2020-05-02T15:45:47",{"id":260,"version":261,"summary_zh":262,"released_at":263},323863,"v5.2.0","#### Improvements\r\n\r\n* [#542](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F542): Introduce `InvertibleCodec` interface. This interface extends the the current `Codec` interface.\r\n```java\r\npublic interface InvertibleCodec\u003CT, G extends Gene\u003C?, G>> extends Codec\u003CT, G> {\r\n    public Function\u003CT, Genotype\u003CG>> encoder();\r\n    public default Genotype\u003CG> encode(final T value) {\r\n        return encoder().apply(value); \r\n    }\r\n}\r\n```\r\n* [#543](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F543): Simplified `Constraint` factory methods.\r\n* [#566](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F566): New parameter class, `EvolutionParams`, contains all `Engine` parameters which influence the evolution performance.\r\n* [#607](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F607): More flexible MOEA optimization. It is now possible to do minimization\u002Fmaximization on every dimension independently.\r\n* [#614](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F614): Generalize the `ConstExprRewriter` class. It can no be used with every type, not only with `Double` values.\r\n* [#635](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F635): Mark the `Chromosome.toSeq()` and `Genotype.toSeq()` methods as deprecated. This methods are no longer needed, because the `Chromosome` and `Genotype` itself will implement the new `BaseSeq` interfaces and are now _sequences_ itself. \r\n* [#645](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F645): Mark all bean-like _getter_ methods as deprecated. This methods will be replaced by simple _accessor_-methods, and is a preparation step for using the new Java _records_.\r\n\r\n\r\n#### Bugs\r\n\r\n* [#621](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F621): `ìo.jenetics.prog.op.Program.arity()` returns the wrong value.\r\n\r\n","2020-02-18T19:50:11",{"id":265,"version":266,"summary_zh":267,"released_at":268},323864,"v5.1.0","#### Improvements\r\n\r\n* [#522](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F522): Replace `io.jenetics.ext.engine.AdaptiveEngine` with `io.jenetics.ext.engine.UpdatableEngine`. The `AdaptiveEngine` has been marked as deprecated.\r\n* [#557](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F557): Implementation `io.jenetics.util.ProxySorter` class, which sorts a proxy array instead of an sequence itself.\r\n* [#563](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F563): Introduction of `Evolution` interface, which makes the _concept_ of an _evolution_ function more explicit.\r\n* [#579](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F579): Improve internal `RingBuffer` implementation.\r\n* [#585](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F585): Improve `EphemeralConst` serialization.\r\n* [#592](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F592): Add `Tree.path()` and `Tree.pathElements()` methods.\r\n\r\n#### Bugs\r\n\r\n* [#539](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F539): Fix JHM tests.\r\n* [#599](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F599): `Recombinator` performs `recombine` on an individual with itself.\r\n* [#600](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F600): Duplicates in Pareto set owing to the `equals` method in `Phenotype` class.\r\n\r\n","2019-11-18T19:44:06",{"id":270,"version":271,"summary_zh":272,"released_at":273},323865,"v5.0.1","#### Bugs\r\n\r\n* [#550](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F550): Erroneous index check for `Sample.argAt(int)` method in `io.jenetics.prog.regression` package. \r\n* [#554](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F550): `ClassCastException` in `io.jenetics.prog.regression.Regression` class. ","2019-08-03T15:28:07",{"id":275,"version":276,"summary_zh":277,"released_at":278},323866,"v5.0.0","#### Improvements\r\n\r\n* [#534](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F534): Generify `Regression` classes so it can be used for regression analysis of arbitrary types.\r\n* [#529](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F529): Implementation of Hybridizing PSM and RSM mutation operator (HPRM)\r\n* [#518](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F518): Implementation of Symbolic Regression classes. This makes it easier to solve such optimization problems.\r\n* [#515](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F515): Rename `Tree.getIndex(Tree)` to `Tree.indexOf(Tree)`.\r\n* [#509](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F509): Allow to collect the nth best optimization results.\r\n```java\r\nfinal ISeq\u003CEvolutionResult\u003CDoubleGene, Double>> best = engine.stream()\r\n    .limit(Limits.bySteadyFitness(50))\r\n    .flatMap(MinMax.toStrictlyIncreasing())\r\n    .collect(ISeq.toISeq(10));\r\n```\r\n* [#504](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F504): Rename `Tree.getChild(int)` to `Tree.childAt(int)`.\r\n* [#500](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F500): Implementation of Reverse Sequence mutation operator (RSM).\r\n* [#497](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F497): Implement Boolean operators for GP.\r\n* [#496](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F496): Implement `GT` operator for GP.\r\n* [#493](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F493): Add dotty tree formatter\r\n* [#488](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F488): Implement new tree formatter `TreeFormatter.LISP`. This allows to create a Lisp string representation from a given `Tree`.\r\n* [#487](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F487): Re-implementation of 'MathTreePruneAlterer'. The new implementation uses the newly introduced Tree Rewriting API, implemented in #442.\r\n* [#486](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F486): Implement `TreeRewriteAlterer`, based on the new Tree Rewriting API.\r\n* [#485](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F485): Cleanup of `MathExpr` class.\r\n* [#484](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F484): The `Tree.toString()` now returns a parentheses string.\r\n* [#481](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F481): The parentheses tree representation now only escapes \"protected\" characters.\r\n* [#469](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F469): Implementation of additional `Evaluator` factory methods.\r\n* [#465](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F465): Remove fitness scaler classes. The fitness scaler doesn't carry its weight.\r\n* [#455](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F455): Implementation of `CompletableFutureEvaluator`.\r\n* [#450](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F450): Improvement of `FutureEvaluator` class.\r\n* [#449](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F449): The `Engine.Builder` constructor is now public and is the most generic way for creating engine builder instances. All other builder factory methods are calling this _primary_ constructor.\r\n* [#447](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F447): Remove evolution iterators. The whole evolution is no performed via streams.\r\n* [#442](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F442): Introduce Tree Rewriting API, which allows to define own rewrite rules\u002Fsystem. This is very helpful when solving GP related problems.\r\n```java\r\nfinal TRS\u003CString> trs = TRS.parse(\r\n    \"add(0,$x) -> $x\",\r\n    \"add(S($x),$y) -> S(add($x,$y))\",\r\n    \"mul(0,$x) -> 0\",\r\n    \"mul(S($x),$y) -> add(mul($x,$y),$y)\"\r\n);\r\n\r\n\u002F\u002F Converting the input tree into its normal form.\r\nfinal TreeNode\u003CString> tree = TreeNode.parse(\"add(S(0),S(mul(S(0),S(S(0)))))\");\r\ntrs.rewrite(tree);\r\nassert tree.equals(TreeNode.parse(\"S(S(S(S(0))))\"));\r\n```\r\n* [#372](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F372): Allow to define the chromosome index an `Alterer` is allowed to change. This allows to define alterers for specific chromosomes in a genotype.\r\n```java\r\n\u002F\u002F The genotype prototype, consisting of 4 chromosomes\r\nfinal Genotype\u003CDoubleGene> gtf = Genotype.of(\r\n    DoubleChromosome.of(0, 1),\r\n    DoubleChromosome.of(1, 2),\r\n    DoubleChromosome.of(2, 3),\r\n    DoubleChromosome.of(3, 4)\r\n);\r\n\r\n\u002F\u002F Define the GA engine.\r\nfinal Engine\u003CDoubleGene, Double> engine = Engine\r\n    .builder(gt -> gt.getGene().doubleValue(), gtf)\r\n    .selector(new RouletteWheelSelector\u003C>())\r\n    .alterers(\r\n        \u002F\u002F The `Mutator` is used on chromosome with index 0 and 2.\r\n        PartialAlterer.of(new Mutator\u003CDoubleGene, Double>(), 0, 2),\r\n        \u002F\u002F The `MeanAlterer` is used on chromosome 3.\r\n        PartialAlterer.of(new MeanAlterer\u003CDoubleGene, Double>(), 3),\r\n        \u002F\u002F The `GaussianMutator` is used on all chromosomes.\r\n        new GaussianMutator\u003C>()\r\n    )\r\n    .build();\r\n```\r\n* [#368](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F368): Remove deprecated code.\r\n* [#364](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F364): Clean implementation of async fitness functions.\r\n* [#342](https:\u002F\u002Fgithub.com\u002Fjenetics\u002Fjenetics\u002Fissues\u002F342): The `Tree` accessor names are no longer in a","2019-06-23T15:27:19"]