Jenetics

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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.

Other languages

• Jenetics.Net: Experimental .NET Core port in C# of the base library.
• Helisa: Scala wrapper around the Jenetics library.

Documentation

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

Requirements

Runtime

• JRE 8: Java runtime version 8 is needed for using the library, respectively for running the examples.

Build time

• JDK 8: The Java JDK 8 must be installed.
• Gradle 5.x: Gradle is used for building the library. (Gradle is download automatically, if you are using the Gradle Wrapper script ./gradlew, located in the base directory, for building the library.)

Test compile/execution

• TestNG 7.x: Jenetics uses TestNG framework for unit tests.
• Apache Commons Math 3.6: Library is used for testing statistical collectors.

Build Jenetics

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 sub-project is located in it’s own sub-directory:

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 evolution Engine.
• 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

Example

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()
            .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:

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 Engine.build 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 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.

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

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

• 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.
• Chartsy, One: Chartsy, One is a Netbeans based tool for stock market investors and traders.
• Chronetic: Chronetic is an open-source time pattern analysis library built to describe time-series data.
• APP4MC: Eclipse APP4MC is a platform for engineering embedded multi- and many-core software systems.

Blogs

• 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

Citations

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• O. M. Elzeki, M. F. Alrahmawy, Samir Elmougy. A New Hybrid Genetic and Information Gain Algorithm for Imputing Missing Values in Cancer Genes Datasets. PInternational Journal of Intelligent Systems and Applications (IJISA), Vol.11, No.12, pp.20-33, DOI: 10.5815/ijisa.2019.12.03. Dec. 2019.
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• Francisco G. Montoya and Raúl Baños Navarro (Eds.). Optimization Methods Applied to Power Systems, Volume 2. MDPI Books, ISBN 978-3-03921-156-2. July 2019.
• Höttger, Robert & Ki, Junhyung & Bui, Bao & Igel, Burkhard & Spinczyk, Olaf. CPU-GPU Response Time and Mapping Analysis for High-Performance Automotive Systems. 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). July 2019.
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• Rodolfo Ayala Lopes, Thiago Macedo Gomes, and Alan Robert Resende de Freitas. A symbolic evolutionary algorithm software platform. Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '19). July 2019.
• Aleksandar Prokopec, Andrea Rosà, David Leopoldseder, Gilles Duboscq, Petr Tůma, Martin Studener, Lubomír Bulej, Yudi Zheng, Alex Villazón, Doug Simon, Thomas Würthinger, Walter Binder. Renaissance: Benchmarking Suite for Parallel Applications on the JVM. PLDI ’19, Phoenix, AZ, USA. June 2019.
• Robert Höttger, Lukas Krawczyk, Burkhard Igel, Olaf Spinczyk. Memory Mapping Analysis for Automotive Systems. Brief Presentations Proceedings (RTAS 2019). Apr. 2019.
• Al Akkad, M. A., & Gazimzyanov, F. F. AUTOMATED SYSTEM FOR EVALUATING 2D-IMAGE COMPOSITIONAL CHARACTERISTICS: CONFIGURING THE MATHEMATICAL MODEL. Intellekt. Sist. Proizv., 17(1), 26-33. doi: 10.22213/2410-9304-2019-1-26-33. Apr. 2019.
• Alcayde, A.; Baños, R.; Arrabal-Campos, F.M.; Montoya, F.G. Optimization of the Contracted Electric Power by Means of Genetic Algorithms. Energies, Volume 12, Issue 7, Apr. 2019.
• Abdul Sahli Fakharudin, Norazwina Zainol, Zulsyazwan Ahmad Khushairi. Modelling and Optimisation of Oil Palm Trunk Core Biodelignification using Neural Network and Genetic Algorithm. IEEA '19: Proceedings of the 8th International Conference on Informatics, Environment, Energy and Applications; Pages 155–158, Mar. 2019.
• Aleksandar Prokopec, Andrea Rosà, David Leopoldseder, Gilles Duboscq, Petr Tůma, Martin Studener, Lubomír Bulej, Yudi Zheng, Alex Villazón, Doug Simon, Thomas Wuerthinger, Walter Binder. On Evaluating the Renaissance Benchmarking Suite: Variety, Performance, and Complexity. Cornell University: Programming Languages, Mar. 2019.
• S. Appel, W. Geithner, S. Reimann, M Sapinski, R. Singh, D. M. Vilsmeier OPTIMIZATION OF HEAVY-ION SYNCHROTRONS USINGNATURE-INSPIRED ALGORITHMS AND MACHINE LEARNING.13th Int. Computational Accelerator Physics Conf., Feb. 2019.
• Saad, Christian, Bernhard Bauer, Ulrich R Mansmann, and Jian Li. AutoAnalyze in Systems Biology. Bioinformatics and Biology Insights, Jan. 2019.
• Gandeva Bayu Satrya, Soo Young Shin. Evolutionary Computing Approach to Optimize Superframe Scheduling on Industrial Wireless Sensor Networks. Cornell University, Dec. 2018.
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• Erich C. Teppan and Giacomo Da Col. Automatic Generation of Dispatching Rules for Large Job Shops by Means of Genetic Algorithms. CIMA 2018, International Workshop on Combinations of Intelligent Methods and Applications, Nov. 2018.
• Pasquale Salzaa, Filomena Ferrucci. Speed up genetic algorithms in the cloud using software containers. Future Generation Computer Systems, Oct. 2018.
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Release notes

5.2.0

Improvements

• #542: Introduce InvertibleCodec interface. This interface extends the the current Codec interface.

public interface InvertibleCodec<T, G extends Gene<?, G>> extends Codec<T, G> {
    public Function<T, Genotype<G>> encoder();
    public default Genotype<G> encode(final T value) {
        return encoder().apply(value); 
    }
}

• #543: Simplified Constraint factory methods.
• #566: New parameter class, EvolutionParams, contains all Engine parameters which influence the evolution performance.
• #607: More flexible MOEA optimization. It is now possible to do minimization/maximization on every dimension independently.
• #614: Generalize the ConstExprRewriter class. It can no be used with every type, not only with Double values.
• #635: 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.
• #645: 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.

Bugs

• #621: ìo.jenetics.prog.op.Program.arity() returns the wrong value.

All Release Notes

License

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

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

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

Used software