EO is a template-based, ANSI-C++ evolutionary computation library which helps you to write your own stochastic optimization algorithms insanely fast.
With the help of EO, you can easily design evolutionary algorithms that will find solutions to virtually all kind of hard optimization problems
... [More], from continuous to combinatorial ones.
Designing an algorithm with EO consists in choosing what components you want to use for your specific needs, just as building a structure with Lego blocks. [Less]
The Genetic Algorithm Utility Library (or, GAUL for short) is a flexible programming library designed to aid in the development of applications that use genetic, or evolutionary, algorithms. It provides data structures and functions for handling and manipulation of the data required for serial and
... [More] parallel evolutionary algorithms. Additional stochastic algorithms are provided for comparison to the genetic algorithms. [Less]
The MOEA Framework is an open source Java library for developing and experimenting with multiobjective evolutionary algorithms (MOEAs) and other general-purpose optimization algorithms and metaheuristics. A number of algorithms are provided out-of-the-box, including NSGA-II, ε-MOEA, GDE3 and MOEA/D.
... [More] In addition, third-party tools like JMetal and PISA directly integrate with the MOEA Framework.
The MOEA Framework targets an academic audience, providing the resources necessary to rapidly design, develop, execute and statistically test optimization algorithms. This includes over 40 test problems from the literature, and a suite of statistical tools for comparing and analyzing algorithm performance. [Less]
ECF is a C++ framework intended for application of any type of evolutionary computation. Current features include:
* parameterless: genotype (individual structure) is the only mandatory parameter
* genetic algorithm genotypes (bitstring, binary encoded real values, floating point vectors
... [More], permutation vectors), genetic programming genotype (tree)
* individuals may contain any genotypes in any number
* algorithms: steady state tournament, generational roulette-wheel, elimination, particle swarm optimization (PSO), differential evolution (DE), artificial bee colony (ABC), clonal selection (CLONALG), genetic annealing, random search
* parallel execution in many models (global paralel EA, distributed EA, hybrid parallel EA...) using MPI [Less]
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