Search Results for "parallel genetic algorithm" - Page 3
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Showing 79 open source projects for "parallel genetic algorithm"
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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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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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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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C++ genetic algorithms library (solving of the NP opnimisation problems). More GA strategies, more useability, more algorithm speed.
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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.
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Galileo is a library for developing custom distributed genetic algorithms developed in Python. It provides a robust set of objects that can be used directly or as the basis of derived objects. Its modularity makes it easy to extend the functionality. The
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The Athlete is a virtual robot living in the ODE (Open Dynamics Engine)world. An evolution algorithm is implemented based on the DR-EA-M to optimize its performances. This involves a Genetic algorithm, network of neurons and morphology. All is in java.
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TA scheduler based on a genetic algorithm to make the tedious task of scheduling TA's more efficient.
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GD is AI - ALife becoming true. It's a genetic engine server, capable to run any kind of genetic algorithm. It has TCP architetcure, working with software clients and uman interaction. It has plugin capabilities and comes with a working Function Finder
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Java port and extension of MLC++ 2.0 by Kohavi et al. Currently contains ID3, C4.5, Naive (aka Simple) Bayes, and FSS and CHC (genetic algorithm) wrappers for feature selection. WEKA 3 interfaces are in development.
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ga2 is a simple C++ library providing the necessary base classes to implement a genetic algorithm in C++. It is based loosely on Goldberg's canonical GA, but with many modifications, improvements and additional features. Essentially feature-complete, and
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Domigene is a genetic algorithm API attempting to incorporate dominant and recessive genes. Supports modular evolution types, genetic programs, definable instruction sets, variation in population, chromosomes, mortality, mutation, fitness functions.
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The goal of the Rats and Mazes project is to build a simple genetic algorithm to find general solutions to random non-cyclic single path mazes. I believe that the solutions will tend towards either the right/left-hand rule.
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aVolve is an evolutionary/genetic algorithm designed to evolve single-cell organisms in a micro ecosystem. It currently uses the JGAP Genetic algorithm, but does include a primitive genetic algorithm written in Python.
