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NEW OPTIMIZATION TECHNOLOGY & PLANNING EXPERIMENT. Technology is designed for multidimensional optimization practical problems with continuous object functions. Technology higher efficiency than traditional optimization methods.
JGAP is a Genetic Algorithms and Genetic Programming package written in Java. It is designed to require minimum effort to use, but is also designed to be highly modular. JGAP features grid functionality and a lot of examples. Many unit tests included.
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Open Metaheuristic (oMetah) is a library aimed at the conception and the rigourous testing of metaheuristics (i.e. genetic algorithms, simulated annealing, ...). The code design is separated in components : algorithms, problems and a test report generator
Framework for development of simple evolutionary algorithms / island models programs in distributed environment using MapReduce programming model based on hadoop.
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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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++.
CGL(C Generic Library) is a stl-like library written in pure C.It implements iterators, memory management,generic containers such as list,map, generic algorithms such as sort() and search(),and it can manipulate any types of data.
a framework library with a template method interface and inheritable classes for an executable process design which produces an executable which is both a GUI and a console application. It provides multi-threading for work items of the provider process .
CuberGA project is the flexible Genetic Algorithms framework. Realize your ideas easy with deliveries of this project. Keywords: genetic algorithms, framework, permutation, mutation, crossover, genotype, selection, survival, Левченко Илья.
A concise example of the classical genetic algorithm, with a fancy windows terminal display. Features DNA editing, save/load, customizable constraints and statistics logging.