Search Results for "matlab genetic algorithm"
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Showing 7 open source projects for "matlab genetic algorithm"
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MultiSpecEPR
MultiSpec EPR handles and present several spectra 1D and 2D
MultiSpec EPR is a program made to handle and to present several spectra (up to 4000) simultaneously or one by one as well as EPR spectra with two dimensions. It loads spectra from Bruker EPR instruments. It has a new algorithm for automatic peak picking which is very efficient and a very fast and accurate line fitting. It makes colorful figures that can be transferred directly to the scientific articles of one or multiple spectra. Save txt tables of the peaks and integrals that can be... -
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GeneticSharp
GeneticSharp is a fast, extensible, multi-platform and multithreading
GeneticSharp is a fast, extensible, multi-platform and multithreading C# Genetic Algorithm library that simplifies the development of applications using Genetic Algorithms (GAs). Can be used in any kind of .NET 6, .NET Standard and .NET Framework apps, like ASP .NET MVC, ASP .NET Core, Blazor, Web Forms, UWP, Windows Forms, GTK#, Xamarin, MAUI and Unity3D games. GeneticSharp and extensions (TSP, AutoConfig, Bitmap equality, Equality equation, Equation solver, Function builder, etc). ... -
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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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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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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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Application to test a GA solution for the Knapsack problem, it will compare Genetic Algorithm solution of the Knapsack problem to greedy algorithm.
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