Search Results for "parallel genetic algorithm" - Page 8
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Showing 237 open source projects for "parallel genetic algorithm"
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The Automatic Model Optimization Reference Implementation, AMORI, is a framework that integrates the modelling and the optimization processes by providing a plug-in interface for both. A genetic algorithm and Markov simulations are currently implemented.
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JNSGA2 is a Java library with an implementation of the multi-objective genetic algorithm NSGA-II published by Deb et al.
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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.
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Searches for adecuate design for feedforward backpropagation neural network, employing genetic algorithm as refining engine. The result topolgy may not be orthodox.
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czip is a unix based utility written for the cell broadband engine. It allows its users to compress files in gzip format. It uses Jean-loup Gailly's deflate algorithm and is based in part on pigz, Mark Adlers parallel version of zlib.
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This project is about an algorithm for multi-resolution texture systhesis on Cell.BE.There are there source code files.One is serial on x86.One is serial code on cell ppu,and the last is parallel code,which use ppu and 8 spus.
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PyLife is an implementation of the game of life algorithm featuring parallel programming. It uses MPI and python to achieve a consistent software architecture and reliably performance.
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A flexible programming library for evolutionary computation. Steady-state, generational and island model genetic algorithms are supported, using Darwinian, Lamarckian or Baldwinian evolution. Includes support for multiprocessor and distributed systems.
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Wodka is the implementation of a genetic algorithm (GA) that can bread two dimensional structures that might be used as robots in sodarace competitions.
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OGA, Open Genetic Algorithm implements the evolutionary genetic algorithms with an user interface in GTK+. The main goal of the program is solve complex problems using evolutionary algorithms like genetic algorithms or evolutionary strategies.
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Galapagos is a Genetic Algorithm 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.
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DrPangloss is a python implementation of a three operator genetic algorithm, complete with a java swing GUI for running the GA and visualising performance, generation by generation
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NullAllEst is the implementation of a maximum likelihood algorithm to estimate the frequency of a null allele in microsatellite genetic data. A Markov Chain Monte Carlo simulation is used to solve the likelihood function.
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gaDesignLib
Genetic algorithm for mechatronic product design
This C++/STL library provides you with the basic genetic algorithms needed for engineering design, such as simple GA and variable population. -
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A Java implementation of the NEAT algorithm as created by Kenneth O Stanley. Also provides a toolkit for further experiments to be created and can provide both local and distributed learning environments.
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musicomp is a program which most important element is an evolutionary algorithm which uses data mining methods as a fitness function to generate monophone melodies.
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Neural networks evolution (genetic algorithm) written in Java. Now neural networks try to playing Go, Tic-Tac-Toe.
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Molevolve is a Java library for running a Genetic Algorithm to model the 3-dimensional structures of peptide chains from amino-acid sequences. Client code can specify its own peptide chain model, fitness functions and GA operations. Requires JDK 1.5.
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Black Box GA provides a user, with a black box program to use a genetic algorithm to help optimize problems, without the user coding a genetic algorithm, or even understanding how a Genetic Algorithm works!
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GALib, Java Genetic Algorithm (JAR) library, models chromosomes as real numbers, characters, strings, and string sequences. Very easy to use, just extend one of the existing GA classes and implement your fitness function. Sample GAs included.
