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Evolving Objects

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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]

0 lines of code

0 current contributors

0 since last commit

6 users on Open Hub

Activity Not Available
5.0
 
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Mostly written in language not available
Licenses: lgpl

PyBrain

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  Analyzed about 11 hours ago

PyBrain is a modular Machine Learning Library for Python. It's goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms. PyBrain is short for Python-Based Reinforcement Learning ... [More] , Artificial Intelligence and Neural Network Library. It's the Swiss army knife for machine learning and neural networking. [Less]

37.1K lines of code

0 current contributors

over 6 years since last commit

6 users on Open Hub

Inactive
5.0
 
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MOEA Framework

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  Analyzed about 14 hours 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 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]

77.7K lines of code

1 current contributors

2 days since last commit

1 users on Open Hub

High Activity
5.0
 
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moses

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  Analyzed about 17 hours 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]

41.5K lines of code

5 current contributors

7 months since last commit

0 users on Open Hub

Very Low Activity
0.0
 
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