Jenetics

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。

其他语言

• Jenetics.Net：用 C# 编写的实验性 .NET Core 版本的基础库移植。
• Helisa：Jenetics 库的 Scala 封装。

星标历史

文档

该库拥有完整的文档（javadoc）和用户手册（pdf）。

构建 Jenetics

Jenetics 至少需要 Java 25 才能编译和运行。

从 GitHub 克隆主分支：

$ git clone https://github.com/jenetics/jenetics.git <builddir>

Jenetics 使用 Gradle 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：

已发布的项目

以下项目/模块也已发布到 Maven 中心仓库：

• jenetics ：该项目包含 Jenetics 核心模块的源代码和测试。
• jenetics.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。
• jenetics.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。
• jenetics.xml ：这是用于 Jenetics 基础数据结构的 XML 序列化模块。

未发布的模块

• 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。
• jenetics.example：该模块包含核心模块的示例代码。
• jenetics.doc：包含网站和手册的代码。
• 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。

要构建库，请进入 <builddir> 目录（或其中一个模块目录），然后运行以下任务之一：

• compileJava：编译 Jenetics 源代码，并将编译后的类文件复制到 <builddir>/<module-dir>/build/classes/main 目录。
• jar：编译源代码并生成 JAR 文件。生成的工件会被复制到 <builddir>/<module-dir>/build/libs 目录。
• javadoc：生成 API 文档。Javadoc 存储在 <builddir>/<module-dir>/build/docs 目录中。
• 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 <builddir>/<module-dir> 目录。
• clean：删除 <builddir>/build/* 目录，并移除所有生成的工件。

要从源代码构建库的 JAR 文件，请执行以下命令：

$ cd <build-dir>
$ ./gradlew jar

示例

你好，世界（统计1的个数）

最小化的进化引擎设置需要一个基因型工厂 Factory<Genotype<?>> 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。

import io.jenetics.BitChromosome;
import io.jenetics.BitGene;
import io.jenetics.Genotype;
import io.jenetics.engine.Engine;
import io.jenetics.engine.EvolutionResult;
import io.jenetics.util.Factory;

public class HelloWorld {
    // 2.) 定义适应度函数。
    private static Integer eval(Genotype<BitGene> gt) {
        return gt.chromosome()
            .as(BitChromosome.class)
            .bitCount();
    }

    public static void main(String[] args) {
        // 1.) 定义适合该问题的基因型（工厂）。
        Factory<Genotype<BitGene>> gtf =
            Genotype.of(BitChromosome.of(10, 0.5));

        // 3.) 创建执行环境。
        Engine<BitGene, Integer> engine = Engine
            .builder(HelloWorld::eval, gtf)
            .build();

        // 4.) 启动执行（进化），并收集结果。
        Genotype<BitGene> result = engine.stream()
            .limit(100)
            .collect(EvolutionResult.toBestGenotype());

        System.out.println("你好，世界:\n" + result);
    }
}

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：

1. 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。

2. 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<BitGene> 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。

3. 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。

4. 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。

进化图像

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：

$ ./gradlew compileTestJava

您可以通过以下命令启动该示例：

$ ./jrun io.jenetics.example.image.EvolvingImages

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。

使用 Jenetics 的项目

• SPEAR: 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。
• Renaissance Suite: 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。
• APP4MC: Eclipse APP4MC 是一个用于嵌入式多核和众核软件系统设计的平台。

博客和文章

• 用进化算法创作国际象棋问题, 作者 Jakob Leck, 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）。
• 使用 Jenetics 库解决背包问题, 作者 Craftcode Crew, 2021年5月13日
• 一种基于Jenetics的遗传算法程序设计, 作者 王康, 《电脑知识与技术》2018年第22期，2018年11月26日
• Jenetics 库简介, 作者 baeldung, 2017年4月11日
• 如何使用遗传算法解决难题, 作者 Tzofia Shiftan, 2017年4月6日
• 使用 Java 的遗传算法, 作者 William Antônio, 2017年1月10日
• Jenetics 安装及示例, 作者 JDM, 2015年5月8日
• 遗传算法（Genetic Algorithms）, 作者 JDM, 2015年4月2日

引用

马尔科·施米德，米哈·拉夫贝尔。《GIANT：通用智能智能体训练器》。DOI: 10.1016/j.softx.2026.102607。《SoftwareX》，第34卷，2026年6月。

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发行说明

9.0.0

改进

• 更新至 Java 25，并针对新版本 Java 优化代码。
• #917：为 RandomRegistry 类引入 ScopedValue。
• #940：移除已弃用的 API。
• #955：使 IntStream 计数更加健壮。

所有发行说明

许可证

本库采用 Apache License, Version 2.0 许可证授权。

Copyright 2007-2026 Franz Wilhelmstötter

根据 Apache License, Version 2.0（“许可证”）授权；
除非符合许可证规定，否则不得使用本文件。
您可以在以下地址获取许可证副本：

http://www.apache.org/licenses/LICENSE-2.0

除非适用法律要求或双方另有约定，否则软件按“原样”分发，
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，
请参阅许可证文本。

使用的软件

Jenetics 快速上手指南

Jenetics 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。

环境准备

• 操作系统：Linux, macOS, Windows
• JDK 版本：Java 25 或更高版本（编译和运行必需）
• 构建工具：Gradle（用于源码构建），或 Maven/Gradle（用于项目依赖管理）

注意：请确保您的 JAVA_HOME 环境变量已正确指向 Java 25+。

安装步骤

方式一：通过 Maven 依赖（推荐）

如果您使用 Maven 构建项目，请在 pom.xml 中添加以下依赖：

<dependency>
    <groupId>io.jenetics</groupId>
    <artifactId>jenetics</artifactId>
    <version>9.0.0</version> <!-- 请使用最新稳定版本 -->
</dependency>

若需使用扩展功能（如多目标优化、语法进化），可额外添加：

<dependency>
    <groupId>io.jenetics</groupId>
    <artifactId>jenetics.ext</artifactId>
    <version>9.0.0</version>
</dependency>

方式二：通过 Gradle 依赖

在 build.gradle 中添加：

dependencies {
    implementation 'io.jenetics:jenetics:9.0.0'
    // implementation 'io.jenetics:jenetics.ext:9.0.0' // 可选扩展模块
}

方式三：源码编译

如果您希望从源码构建：

git clone https://github.com/jenetics/jenetics.git <builddir>
cd <builddir>
./gradlew jar

编译后的 JAR 文件位于 <builddir>/<module-dir>/build/libs 目录下。

基本使用

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。

代码示例

import io.jenetics.BitChromosome;
import io.jenetics.BitGene;
import io.jenetics.Genotype;
import io.jenetics.engine.Engine;
import io.jenetics.engine.EvolutionResult;
import io.jenetics.util.Factory;

public class HelloWorld {
    // 2. 定义适应度函数 (计算 1 的个数)
    private static Integer eval(Genotype<BitGene> gt) {
        return gt.chromosome()
            .as(BitChromosome.class)
            .bitCount();
    }

    public static void main(String[] args) {
        // 1. 定义基因型工厂 (创建一个长度为 10 的二进制染色体)
        Factory<Genotype<BitGene>> gtf =
            Genotype.of(BitChromosome.of(10, 0.5));

        // 3. 创建进化引擎
        Engine<BitGene, Integer> engine = Engine
            .builder(HelloWorld::eval, gtf)
            .build();

        // 4. 启动进化流并收集结果 (限制为 100 代)
        Genotype<BitGene> result = engine.stream()
            .limit(100)
            .collect(EvolutionResult.toBestGenotype());

        System.out.println("Hello World:\n" + result);
    }
}

核心步骤解析

1. 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）。
2. 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。
3. 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。
4. 执行进化 (EvolutionStream)：
   • 调用 engine.stream() 获取进化流。
   • 利用 Java Stream API 控制流程（如 .limit(100) 限制进化代数）。
   • 使用 EvolutionResult.toBestGenotype() 收集最佳结果。

Jenetics 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）。