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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]

5.0
 
  0 reviews  |  6 users  |  60,764 lines of code  |  7 current contributors  |  Analyzed 5 days ago
 
 

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 ... [More] MOEA/D. 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]

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  0 reviews  |  1 user  |  0 current contributors
 
 

Thesis project, graph(vertex) coloring problem with genetic programming binary

5.0
 
  0 reviews  |  0 users  |  15,529 lines of code  |  1 current contributor  |  Analyzed 12 days ago
 
 
 
 

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