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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]

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  0 reviews  |  6 users  |  60,764 lines of code  |  7 current contributors  |  Analyzed 3 days ago
 
 

Meta-optimizing semantic evolutionary search (MOSES) is a new approach to program evolution, based on representation-building and probabilistic modeling. MOSES has been successfully applied to solve hard problems in domains such as computational biology, sentiment evaluation, and agent control. ... [More] Results tend to be more accurate, and require less objective function evaluations, in comparison to other program evolution systems. Best of all, the result of running MOSES is not a large nested structure or numerical vector, but a compact and comprehensible program written in a simple Lisp-like mini-language. For more information see: http://metacog.org/doc.html. Interested C++ developers, please drop in at #opencog on IRC.freenode.net. [Less]

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  0 reviews  |  0 users  |  24,782 lines of code  |  0 current contributors  |  Analyzed 6 days ago
 
 
 
 

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