Search Results for "knapsack genetic algorithm" - Page 2
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Showing 64 open source projects for "knapsack genetic algorithm"
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Genetic Algorithm General Solver-GAGENES
Genetic algorithms (GA) in C++
NOTE: Project has been delayed due to other tasks. Genetic Algorithms General Solver (GAGENES) is a C++ implementation of the genetic algorithm concept. -
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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.
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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.
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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.
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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 genetic algorithm, and more.
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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.
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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 Genetic Algorithm (GA). Created for use in M.S. theses
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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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An implementation of LAYAGEN G(Diego-Mas 2010) for solves the layout planning problem using a simple genetic algorithm, and fully written in GAMBAS
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This is implementation of parallel genetic algorithm 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++.
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In this project we develop a software, that uses evolutionary algorithm to evolve a solution, that can drive a car on an arbitrari path.
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A .net implementation of a framework for genetic algorithms. This tool enables programmers to write the "core" of their problem and have a genetic algorithm immediately setup for solving it.
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A Tcl extension for genetic/evolutionary algorithm processing. It is using the GAUL library for the core functionality.
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Evolve is a simulation written in java that uses the Genetic Algorithm to evolve intelligent agents acting in a rich environment.
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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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A 2D rigid physics simulator and set of genetic algorithm tools. Evolve cute little walking creatures.
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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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spGAL is a C++ Genetic Algorithm (GA) library. This is a portable scalable, and easy to use library written with ANSI C++.
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A concise example of the classical genetic algorithm, with a fancy windows terminal display. Features DNA editing, save/load, customizable constraints and statistics logging.
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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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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.