Search Results for "genetic algorithm and fortran"
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Showing 8 open source projects for "genetic algorithm and fortran"
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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). A Blazor... -
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[OFFICIAL] tinyfiledialogs C C++ v3.19.3
inputbox password WIN OSX GTK QT Console VCPKG C# Lua R Fortran Pascal
... ASCII UTF-8 - applescript kdialog zenity … ° SSH console / X forwarding / waypipe Wayland ° the dialogs can be forced into console mode ° curses dialogs via Dialog.exe/Dialog (disabled by default) C89/C18 & C++98/C++23 compliant VisualStudio MinGW GCC Clang TinyCC IntelCC OWCC BCC SunCC on Windows Mac Linux Bsd Solaris Minix Raspbian Flatpak bindings: VCPKG C# R Lua Rust Haskell Java AllegroBasic Fortran Pascal D JavaScript Kotlin click tab 'Files' for prototypes -
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This site hosts the source code for C++ version of the Broker for SBW, NOM module, advanced simulation suite, analysis applications and model editors.
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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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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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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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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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