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A GPU-based efficient data parallel formulation of the kNN problem
A GPU-based efficient data parallel formulation of the k-Nearest Neighbor (kNN) search problem which is a popular method for classifying objects in several fields of research, such as- pattern recognition, machine learning, bioinformatics etc.
NOTE: Project has been delayed due to other tasks.
Genetic Algorithms General Solver (GAGENES) is a C++ implementation of the geneticalgorithm concept.
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.
X-GAT (XML-based GeneticAlgorithm 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.
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This library is a lightweight implementation of geneticalgorithm, contains the most popular types of chromosomes and the basic algorithms for selection, elitism, crossing and mutation.
Game Toolbox is a C# assembly designed to facilitate the creation of games and game prototypes. It contains no graphics code, does not depend on XNA, and is Mono-compatible. It provides implementations of A*, GOAP, a geneticalgorithm, and more.
Evolutionary Structural Optimization Package (ESOP) consists of software for viewing, analyzing, and optimizing structures containing beam, truss, and membrane plate elements utilizing OpenGL and the GeneticAlgorithm (GA). Created for use in M.S. theses
Geneur is an Open Source scheduler for GRID. It is based on variation of genetic algorithms. Geneur uses backfill scheduling algorithm to create first genetic population.
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.
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This is implementation of parallelgeneticalgorithm with "ring" insular topology. Algorithm provides a dynamic choice of genetic operators in the evolution of. The library supports the 26 genetic operators. This is cross-platform GA written in С++.
A .net implementation of a framework for genetic algorithms. This tool enables programmers to write the "core" of their problem and have a geneticalgorithm immediately setup for solving it.
GEP is an evolutionary algorithm for function finding. This framework is a powerful way of expressing and coding genetic-like structures and quickly finding solutions through evolution by common genetic operators.
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.
A concise example of the classical geneticalgorithm, with a fancy windows terminal display. Features DNA editing, save/load, customizable constraints and statistics logging.
Galapagos is a GeneticAlgorithm framework written in Java 5 with the intended audience of undergraduates in an Artificial Intelligence class. The goal of Galapagos is usability: a competent student should be able to learn this library in an afternoon.