Open Source Java Genetic Algorithms

JGAP is a Genetic Algorithms and Genetic Programming package written in Java. It is designed to require minimum effort to use, but is also designed to be highly modular. JGAP features grid functionality and a lot of examples. Many unit tests included. Legal notice/Impressum: Klaus Meffert An der Struth 25 D-65510 Idstein sourceforge <at> klausmeffert.de

JAG3D is no longer developed at source-forge, and has moved to GitHub. Please visit https://github.com/applied-geodesy/jag3d or https://software.applied-geodesy.org to get the latest version.

The source code has been migrated and is now hosted on Github: https://github.com/jenetics/jenetics Jenetics is an advanced Genetic Algorithm, Evolutionary Algorithm and Genetic Programming library, respectively, written in modern day Java. It is designed with a clear separation of the several algorithm concepts, e. g. Gene, Chromosome, Genotype, Phenotype, Population and fitness Function. Jenetics allows you to minimize or 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 repositories: - https://github.com/jenetics/jenetics Maven: - https://mvnrepository.com/artifact/io.jenetics

This project contains a solution for a Bin Packing problem solved using Genectic Algorithms. The code in the project was created as a solution for a problem in a combinatorial optimization class at the Univeridade Federal do Rio Grande do Sul (UFRGS - Brasil) in 2007.

This site hosts the source code for C++ version of the Broker for SBW, NOM module, advanced simulation suite, analysis applications and model editors.

CILib is a framework for developing Computational Intelligence software in swarm intelligence, evolutionary computing, neural networks, artificial immune systems, fuzzy logic and robotics.

Flexible job shop scheduling problem (FJSP) is very important in many fields such as production management, resource allocation and combinatorial optimization. In the real manufacturing systems, each operation could be processed on more than one machine and each machine can also process several operations. This feature is known as flexibility. You can define your problem in this software and get an optimal solution as a Gantt Chart. This software is based on my M.Sc. thesis of Shahed university (Tehran, Iran).

This project is a prototype of evolutionary computation algorithms implementations for Android. We, Tom BERNARD and Ugo PICHE, were asked to do this project as part of our electronic & computer science in the Polytech'Paris-UPMC engineer school, in France. We wanted to thank Mario Zechner and all contributors behind the fabulous libGDX project (https://code.google.com/p/libgdx/) which allowed us to use Box2D and OpenGL ES more, more easily than without this API.

The Distributed Genetic Programming Framework is a scalable Java genetic programming environment. It comes with an optional specialization for evolving assembler-syntax algorithms. The evolution can be performed in parallel in any computer network.

OpenDino is an open source Java platform for optimization, design of experiment and learning. It provides a graphical user interface (GUI) and a platform which simplifies integration of new algorithms as "Modules". Implemented Modules Evolutionary Algorithms: - CMA-ES - (1+1)-ES - Differential Evolution Deterministic optimization algorithm: - SIMPLEX Learning: - a simple Artificial Neural Net Optimization problems: - test functions - interface for executing other programs (solvers) - parallel execution of problems - distributed execution of problems via socket connection between computers Others: - data storage - data analyser and viewer

Search Agents - a framework for collaboration of meta-heuristic agents

a framework library with a template method interface and inheritable classes for an executable process design which produces an executable which is both a GUI and a console application. It provides multi-threading for work items of the provider process .

a sandbox for trying my engine ideas

Agenda desenvolvida em Java, utilizando banco PostGreSQL. Itens: Cadastro de Contatos Cadastro de Tarefas

The Gene Expression Programming Framework in Java. It separates the process of evolution from the process of interpretation of the chromosome, allowing the use of various schemes in the chromosome.

The main goal of the Analytical Customer Relationship Management system is to improve services provided to customers, and to use customer contact information for targeted marketing.

Test of Artificial Life

Awakener aims to provide a Java library for solving practical, real world optimisation problems by means of genetic algorithms (turnkey algorithms for >= 90% of industry problems). Awakener extends Sleepwalker with specific algorithms.

Beagle is Java open source framework for running and managing nondeterministic algorithms such as genetic algorithms for solving complex problems. Beagle is fully modular and maintains whole history of algorithm progress for further analysis.

It moves by itself inside networks like virus infection & plagues, it is being written to solve computer virus problem drastically and responsibly. It is legal, free and open for public domain to improve W3 ICT Security.

Calamari is a collection of Java APIs for implementing genetic algorithms, neural networks, and vehicle simulation. It is still being designed and developed.

Cutting Problem solved by Genetic algorithms. The goal is to cut a rectangular plate of material into more smaller rectangles. The cuts must be rectangular and guillotinable.

This chess program changes its strength to give the best match against you. Eventually it learns to beat you specifically through learning alogirthms. Features included transposition tables and a elementary 3-piece endgame tablebase.

Classical genetic algorithm suffers heavy pressure of fitness evaluation for time-consuming optimization problems. To address this problem, we present an efficient genetic algorithm by the combination with clustering methods. The high efficiency of the proposed method results from the fitness estimation and the schema discovery of partial individuals in current population and. Specifically, the clustering method used in this paper is affinity propagation. The numerical experiments demonstrate that the proposed method performs promisingly for well-known benchmark problems in the term of optimization accuracy.

EZvolve Foundation Classes is a set of data types and utility classes for use with evolutionary algorithms. Currently implemented support for bit string encoding, populations, fitnesses, fitness functions, probabilities, and probability vectors.