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The Open Cognition Framework (OpenCog) is software for the collaborative development of safe and beneficial Artificial General Intelligence. OpenCog provides research scientists and software developers with a common platform to build and share artificial intelligence programs. Programs written ... [More]
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]
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 ... [More]
The SAT4J project is meant to provide SAT technologies to Java developers. SAT4J 2.0 is currently used in numerous academic projects (see SAT4J web site) and is used in the new Eclipse 3.4 update manager (Equinox p2).
FREVO is an open-source framework developed in Java to help engineers and scientists in evolutionary design or optimization tasks. The major feature of FREVO is the componentwise decomposition and separation of the key building blocks for each optimization tasks. We identify these as the problem ... [More]
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]
METSlib is an OO (Object Oriented) metaheuristics framework in C++. Model and algorithms are modular: all the implemented search algorithms can be applied to the same model and personalized algorithms can be applied to very different models. METSlib implements the basics of some metaheuristics ... [More]
This project is a joint collaboration between particle physicists and AI scientists.The goal is to find selectors suitable for detection of Higgs boson during LHC experiments, maximizing the figure of merit of the selectors. Later on more optimization/learning methods looking for good selectors will be investigated and compared with each other.
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