Jenetics is a 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.
Other languages
- Jenetics.Net: Experimental .NET Core port in C# of the base library.
- Helisa: Scala wrapper around the Jenetics library.
The library is fully documented (javadoc) and comes with an user manual (pdf).
Jenetics requires at least Java 17 to compile and run.
Check out the master branch from GitHub.
$ git clone https://github.com/jenetics/jenetics.git <builddir>
Jenetics uses Gradle as build system and organizes the source into sub-projects (modules). Each subproject is located in its own subdirectory:
Published projects
The following projects/modules are also published to Maven.
- jenetics
: This project contains the source code and tests for the Jenetics core-module.
- jenetics.ext
: This module contains additional non-standard GA operations and data types. It also contains classes for solving multi-objective problems (MOEA).
- jenetics.prog
: The modules contains classes which allows to do genetic programming (GP). It seamlessly works with the existing
EvolutionStream
and evolutionEngine
. - jenetics.xml
: XML marshalling module for the Jenetics base data structures.
Non-published projects
- jenetics.example: This project contains example code for the core-module.
- jenetics.doc: Contains the code of the web-site and the manual.
- 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.
For building the library change into the <builddir>
directory (or one of the module directory) and call one of the available tasks:
- compileJava: Compiles the Jenetics sources and copies the class files to the
<builddir>/<module-dir>/build/classes/main
directory. - jar: Compiles the sources and creates the JAR files. The artifacts are copied to the
<builddir>/<module-dir>/build/libs
directory. - javadoc: Generates the API documentation. The Javadoc is stored in the
<builddir>/<module-dir>/build/docs
directory - 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
<builddir>/<module-dir>
directory. - clean: Deletes the
<builddir>/build/*
directories and removes all generated artifacts.
For building the library jar from the source call
$ cd <build-dir>
$ ./gradlew jar
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()
.as(BitChromosome.class)
.bitCount();
}
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)
.build();
// 4.) Start the execution (evolution) and
// collect the result.
Genotype<BitGene> result = engine.stream()
.limit(100)
.collect(EvolutionResult.toBestGenotype());
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:
-
The probably most challenging part, when setting up a new evolution
Engine
, is to transform the problem domain into a appropriateGenotype
(factory) representation. In our example we want to count the number of ones of aBitChromosome
. Since we are counting only the ones of one chromosome, we are adding only oneBitChromosome
to ourGenotype
. In general, theGenotype
can be created with 1 to n chromosomes. -
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 theChromosome<BitGene>
class to the actual usedBitChromosome
class. Since we know for sure that we created the Genotype with aBitChromosome
, this can be done safely. A reference to the eval method is then used as fitness function and passed to theEngine.build
method. -
In the third step we are creating the evolution
Engine
, which is responsible for changing, respectively evolving, a given population. TheEngine
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 usedExecutor
service, you control the number of threads, the Engine is allowed to use. An newEngine
instance can only be created via its builder, which is created by calling theEngine.builder
method. -
In the last step, we can create a new
EvolutionStream
from ourEngine
. TheEvolutionStream
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, theEvolutionStream
will not terminate and run forever. Since theEvolutionStream
extends thejava.util.stream.Stream
interface, it integrates smoothly with the rest of the Java Stream API. The final result, the bestGenotype
in our example, is then collected with one of the predefined collectors of theEvolutionResult
class.
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
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 storing polygonized images in PNG format to disk. At the button of the UI, you can change some GA parameters of the example.
- SPEAR: SPEAR (Smart Prognosis of Energy with Allocation of Resources) created an extendable platform for energy and efficiency optimizations of production systems.
- Renaissance Suite: Renaissance is a modern, open, and diversified benchmark suite for the JVM, aimed at testing JIT compilers, garbage collectors, profilers, analyzers and other tools.
- APP4MC: Eclipse APP4MC is a platform for engineering embedded multi- and many-core software systems.
- Solving the Knapsack Problem with the Jenetics Library, by Craftcode Crew, May 13, 2021
- 一种基于Jenetics的遗传算法程序设计, 电脑知识与技术 2018年22期 by 王康, Nov. 26. 2018
- Introduction to Jenetics Library, by baeldung, April 11. 2017
- How to Solve Tough Problems Using Genetic Algorithms, by Tzofia Shiftan, April 6. 2017
- Genetic algorithms with Java, by William Antônio, January 10. 2017
- Jenetics 설치 및 예제, by JDM, May 8. 2015
- 유전 알고리즘 (Genetic Algorithms), by JDM, April 2. 2015
Abdessamed Ouessai, Mohammed Salem, Antonio M. Mora. Evolving action pre-selection parameters for MCTS in real-time strategy games. Entertainment Computing, Volume 42. April 2022.
...
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- #632: Convert data classes to
records
. - #696: Convert libraries to JPMS modules.
- #715: Improve
BitChromosome
. - #762: Breaking change Update to Java 17.
- #767: Incubator - Grammar-based evolution.
- #773: Incubator - Simplify and unify parsing code for
MathExpr
class. - #785: Using
RandomGenerator
instead ofRandom
class. - #787: Breaking change - Change upper limit of
Integer
/LongeGenes
from inclusively to exclusively. - #789: Make
AbstractChromosome
non-Serializable
. - #796: Use
InstantSource
instead ofClock
for measuring evolution durations. - #798: Performance improve of subset creation method.
- #801: Introduce
Self
interface. - #816: Add Sudoku example (by alex-cornejo).
- #791: Fix possible overflow in Integer/LongGene mean method.
- #794: Fix possible underflow in DoubleGene mean method.
- #803: Bug checking Sample arity in class SampleList.
The library is licensed under the Apache License, Version 2.0.
Copyright 2007-2022 Franz Wilhelmstötter
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
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