Search Results for "parallel genetic algorithm" - Page 3
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Showing 92 open source projects for "parallel genetic algorithm"
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GAAF is a tool for analyzing Genetic Algorithms (GA for short). It allows to check the behavior of a particular GA resolving a particular problem so one can get empirical information to decide which GA best fits problem's conditions.
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This is C++ application code that implements Gene Expression Programming, or GEP - a form of genetic algorithm.
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Java API for implementing any kind of Genetic Algorithm and Genetic Programming applications quickly and easily. Contains a wide range of ready-to-use GA and GP algorithms and operators to be plugged-in or extended. Includes Tutorials and Examples.
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Springbots is a python application which takes a set of 2d physical structures built with nodes and movable springs and evolve them for specific tasks like walking, swimming and jumping using genetic algorithm.
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TrimGA is a lightweight genetic algorithm library written in pure Java 6.0 that can be quickly applied to most optimization problems.
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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.
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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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This project aims to create an application to solve the job shop schedule problem using genetic algorithm on the IBM Cell BE processor. This is useful especially using the power of Cell for the large scale job shop schedule problem.
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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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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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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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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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unEvo is an Eclipse plug-in that provides support for the experimentation and research process on Evolutionary Algorithms, intended so that the user can implement an evolutionary algorithm without lost time in the code of the algorithm.
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C++ genetic algorithms library (solving of the NP opnimisation problems). More GA strategies, more useability, more algorithm speed.
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EasyAI is a project to build classical AI program and method in Java language, such as resolution for logic, greedy search, genetic algorithm, neural network and so on...
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IslandEv distributes a Genetic Algorithm (like <a href="/projects/jaga">JaGa</a>) across a network (see <a href="/projects/distrit">DistrIT</a>) using an island based coevolutionary model in which neighbouring islands swap migrating individuals every
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Gazoo is a Java framework for genetic algorithms development. Gazoo provides the core of a genetic algorithm, leaving to the user the implementation of specific-problem classes.
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Solving the travelling salesman problem with genetic (evolutionary) algorithms. The distance calculations are based on geographical coordinates. The progress of the algorithm is visualized with a geo-map and some statistics.
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GACS (Genetic Algorithm Class Scheduler) aims to provide to academic institutions an efficient and automatic way to generate their time scheduling for the classes that will be imparted in an academic period.
