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This project provides a set of Python tools for creating various kinds of neural networks, which can also be powered by genetic algorithms using grammatical evolution. MLP, backpropagation, recurrent, sparse, and skip-layer networks are supported.
Simple POSIX multithread Ant Colony Optimizer framework currently for solving Vehicle Routing Problem. Supports UDP, file synchronizatoin between computers.
NaruGo is game AI project. Current targets are GO board game and Texas Holdem poker. It investigates Genetic programming to build game AI logic. Also EA/GP simulations for TSP, Graph layout and Prisoners Dilemma problem.
Open Metaheuristic (oMetah) is a library aimed at the conception and the rigourous testing of metaheuristics (i.e. genetic algorithms, simulated annealing, ...). The code design is separated in components : algorithms, problems and a test report generator
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Testbed for playing with the algorithm "pagerank" of Google
Provide a testbed to play with pagerank-like algorithm on graph. You can easily add vertices, edges, save the graph for reuse, etc. For now, only Pagerank is implemented, but in the future, other algorithms will be added.
Framework for development of simple evolutionary algorithms / island models programs in distributed environment using MapReduce programming model based on hadoop.
A self-contained, fully configurable Java "game" to simulate multi-species evolution. Design species by optionally specifying every attribute; modify any or all environmental settings; let them loose to eat, fight, procreate, die, and Evolve!
GeneThello (read jə-ˈne-ˈthe-lō), is an acronym for genetic othello, an othello (reversi) playing program which based on Genetic Algorithm (GA). In principle GeneThello consist of an othello program and a genetic algorithm system.
A generic implementation of STL containers and some STL algorithms. The main intent is no make this STL implementation to work with any kind of pointers defined by allocator classes, e.g. memory_mgr::offset_ptr.
RobGP is a genetic programming system written from the ground up in C++. It's primary goals are efficiency, ease of use, and extensibility. It's distinguishing feature is that it has a modified version of Koza's architecture altering operations.
The Mars Rover Simulator project is based on the evolutionary robotics paradigm where an artificial agent acquires its skills through the process of artificial evolution. This simulator can be useful to evolve neural network controllers for the rover
NOTE: Project has been delayed due to other tasks.
Genetic Algorithms General Solver (GAGENES) is a C++ implementation of the genetic algorithm concept.
X-GAT (XML-based Genetic Algorithm Toolkit) is a Java framework to optimize problems with Genetic Algorithms (GAs). Differently from other frameworks, X-GAT contains ready-to-use GAs implementations and new features can be easily added.
Data types and utility classes for use with evolutionary algorithms.
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.
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.
Sleepwalker aims to provide a highly abstract, universal, reusable, extensible Java-based genetic algorithms framework which can be used as a basis for modelling and programming virtually any practical optimisation problem.
http://jocdelavida.piposerver.com - Online implementation of Conway's Game of the Life 0 players game, but using entities based on the nature, like animals or plants who born, grow up, reproduces breed and die. Written in Java 6 and Adobe Flex 3.
An interactive binary search tree. The user may interact with the tree by performing rotations, balancing, insertions, and deletions. For educational purposes
This library is a lightweight implementation of genetic algorithm, contains the most popular types of chromosomes and the basic algorithms for selection, elitism, crossing and mutation.
Based on the introduction of Genetic Algorithms in the excellent book "Collective Intelligence" I have put together some python classes to extend the original concepts.