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Jenetics is an Genetic Algorithm, Evolutionary Algorithm, 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.

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The library is fully documented (javadoc) and comes with an user manual (pdf).



Build time

Test compile/execution

Build Jenetics

Check out the master branch from Github.

$ git clone <builddir>

Jenetics uses Gradle as build system and organizes the source into sub-projects (modules). Each sub-project is located in it’s own sub-directory:

Published projects

The following projects/modules are also published to Maven.

Non-published projects

For building the library change into the <builddir> directory (or one of the module directory) and call one of the available tasks:

For building the library jar from the source call

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


Hello World (Ones counting)

The minimum evolution Engine setup needs a genotype factory, Factory<Genotype<?>>, 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.

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.) Definition of the fitness function.
    private static Integer eval(Genotype<BitGene> gt) {
        return gt.chromosome()

    public static void main(String[] args) {
        // 1.) Define the genotype (factory) suitable
        //     for the problem.
        Factory<Genotype<BitGene>> gtf =
            Genotype.of(BitChromosome.of(10, 0.5));

        // 3.) Create the execution environment.
        Engine<BitGene, Integer> engine = Engine
            .builder(HelloWorld::eval, gtf)

        // 4.) Start the execution (evolution) and
        //     collect the result.
        Genotype<BitGene> result =

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

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 streaming API. Now let's have a closer look at listing above and discuss this simple program step by step:

  1. 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.

  2. 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 calculate it's fitness value. If we want to use the optimized bit-counting method, bitCount(), we have to cast the Chromosome<BitGene> 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 method.

  3. 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. An new Engine instance can only be created via its builder, which is created by calling the Engine.builder method.

  4. 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 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.

Evolving images

This example tries to approximate a given image by semitransparent polygons. It comes with an Swing UI, where you can immediately start your own experiments. After compiling the sources with

$ ./gradlew compileTestJava

you can start the example by calling

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

Evolving images

The 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 to store polygonized images in PNG format to disk. At the button of the UI, you can change some of the GA parameters of the example.

Projects using Jenetics

Blogs and articles


Release notes





final Codec<Tree<Op, ?>, ProgramGene> codec = Regression.codecOf(OPS, TMS, 5, t -> t.gene().size() < 30);

final Regression regression = Regression.of( codec, Error.of(LossFunction::mse), sampling );

final Engine<ProgramGene, Double> engine = Engine .builder(regression) .interceptor(nullifier) .build(); .flatMap(Streams.toIntervalMax(Duration.ofSeconds(30))) .map(program -> program.bestPhenotype() .genotype().gene() .toParenthesesString()) // Printing the best program found so far every 30 seconds. .forEach(System.out::println);

* [#671]( Adding helper methods in `Streams` class, which allows to emit the best evolution result of every _n_ generation.
final ISeq<Integer> values = IntStream.range(0, streamSize).boxed()


_All Release Notes_


The library is licensed under the Apache License, Version 2.0.

Copyright 2007-2020 Franz Wilhelmstötter

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
See the License for the specific language governing permissions and
limitations under the License.

Used software