Search Results for "parallel genetic algorithm"

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). ...

CRFSharp(aka CRF#) is a .NET(C#) implementation of Conditional Random Fields, an machine learning algorithm for learning from labeled sequences of examples. It is widely used in Natural Language Process (NLP) tasks, for example: word breaker, postagging, named entity recognized, query chunking and so on. CRF#'s mainly algorithm is the same as CRF++ written by Taku Kudo. It encodes model parameters by L-BFGS. Moreover, it has many significant improvement than CRF++, such as totally parallel encoding, optimizing memory usage and so on. ...

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.

In this project we develop a software, that uses evolutionary algorithm to evolve a solution, that can drive a car on an arbitrari path.

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.

GAPhoto is an image vectorizer in C#.NET using Genetic Algorithims and Parallel Processing via JQFramework. It currently reads JPG,PNG,GIF files and exports to SVG.

Cloudster is a generic and parallel implementation of the k-means algorithm. It is written in C# and uses the Windows Azure environment.

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.

Application to test a GA solution for the Knapsack problem, it will compare Genetic Algorithm solution of the Knapsack problem to greedy algorithm.