Open Source Algorithms - Page 24

The Game of Life involves cell reproduction based on its neightbors, with later modifications taking age and sickness into account.

Gamoliyas is an open source John Conway's Game of Life game totally written in DHTML (JavaScript, CSS and HTML). Uses mouse and keyboard. Very configurable. This cross-platform and cross-browser game was tested under BeOS, Linux, *BSD, Windows and other

Classroom allocation using Genetic Algorithms and restricted mutation. Developed by AIGroup: Pablo Cababie, Facundo Cancelo, Alvaro Zweig y Gabriel Barrera

The package is a Microsoft .NET 2.0 package providing the processing of the geographical data. The library supports popular "home" formats of geographical data: Ozi Explorer, MP and MPS. The library supports map nomenclature (Soviet headquarters).

The C++ library Geneva allows to run large scale parametric optimization problems. It can run in serial or multi-threaded mode or in a networked environment. The library currently covers Evolutionary Strategies, Genetic Algorithms and mixed scenarios.

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.

GeneThello (read jə-ˈne-ˈthe-lō), is an acronym for genetic othello, an othello (reversi) playing program which based on Genetic Algorithm (GA). In principle GeneThello consist of an othello program and a genetic algorithm system.

This is a method for identifying graphs using MD5 hashing. It can be used for graph equality comparisons and to facilitate graph isomorphism testing. The graphs can be labeled or unlabeled.

A generic implementation of STL containers and some STL algorithms. The main intent is no make this STL implementation to work with any kind of pointers defined by allocator classes, e.g. memory_mgr::offset_ptr.

Implementation of paralell partitioning algorithms achieving an optimal number of comparisons. See L. Frias and J. Petit. Parallel partition revisited. http://dx.doi.org/10.1007/978-3-540-68552-4_11 for further reference.

NOTE: Project has been delayed due to other tasks. Genetic Algorithms General Solver (GAGENES) is a C++ implementation of the genetic algorithm concept.

This project is a genetic algorithm engine able to be reused for other projects with minimal additional programming. The genetic algorithm engine currently plays many blackjack hands for the fitness function and produces a result similar to blackjack basic strategy. To see it in action, download the zip file and run either: GABlackjack_Demo.exe     (quick)   or GABlackjack_Long.exe       (slow, but it achieves better results). The code was written in C++, using MS Visual Studio 6.0 and MS Visual Source Safe 6.0. The genetic algorithm engine supports various mutation rates, ranked parental selection, stochastic sampling parental selection, cyclic crossover, crossover at each gene, cloning the best individual each generation, and creating random individuals each generation. To use the genetic algorithm engine to search for a different problem's solution, one needs to program a fitness function, the project settings, and a few virtual functions.

This is a cross-platform framework for using Genetic Algorithms for solutions. Written in Java and uses convinient plug-in features for every phase in the genetic development, while maintaining an easy-to-use API for easy integration into applications.

This API is intended to help developers use genetic algorithms in their own java applications. GeneticLibrary.zip contains the netbeans project of the API itself. GeneticTrial.zip contains another netbeans project which is an example explaining how to use the API. docs.zip is the documentation of the API. Please take a look there, it is really helpful. I have only two request from who will use this API; USE, UPDATE AND MODIFY IT FREELY JUST NOTE THE ORIGINAL AUTHOR

This project aims to create an open source genetic algorithms (GA) library for the IBM Cell BE processor. A well-documented implementation of GAs is provided, along with a number of sample fitness functions illustrating how to use the GA library.

GFP use Genetic Programming and LISP-like language for growing program.

This is an attempt to use Genetic Programming for writing a go player in a pseudo-ASM. It is entirely written in Common Lisp.

GP@home is a new group/project dedicated to fully utilizing the combined concepts of genetic programming and distributed computing to improve the quality of life for all human beings. Nothing is impossible working together.

Genetic Programming Classifier is a distributed evolutionary data classification program. It uses the ensemble method implemented under a parallel co-evolutionary Genetic Programming technique.

Genetic Programming (tree structure) predictor within Weka data mining software for both continuous and classification problems.

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.

Genetic Programming in OpenCL is a parallel implementation of genetic programming targeted at heterogeneous devices, such as CPU and GPU. It is written in OpenCL, an open standard for portable parallel programming across many computing platforms.

The SP is a tool is a tool to research for automatic synthesis of protocols using genetic programming. This tool will allow the construction of heuristics to apply techniques simulation and validation of models.

This project aims at providing a set of tools for solving the class of monodimensional packing problems (such as cutting stock, bin packing and knapsack problem) mainly using genetic algoritms.