Torch is a machine learning library written in C++ that works on most Unix/Linux platforms. It can be used to train MLPs, RBFs, HMMs, Gaussian Mixtures, Kmeans, Mixtures of experts, Parzen Windows, KNN, and can be easily extended so that you can add your own machine learning algorithms.
EAsea Specification of Evolutionary Algorithms (EASEA), is a high-level language dedicated to the specification of evolutionary algorithms. The language and compiler are quite mature. EASEA compiles .ez specification files into C++ or Java object files, using existing evolutionary libraries. Supported C++ libraries currently are GALib or EO.
PyStem is a fast Python module with the the Porter stemming algorithm (a process for removing the commoner morphological and inflexional endings from words in English; its main use is as part of a term normalisation process that is usually done when setting up Information Retrieval systems).
DREAM (The Distributed Resource Evolutionary Algorithm Machine) seeks to provide the technology and software infrastructure necessary to support the next generation of evolving infohabitants in a way that makes that infrastructure universal, open, and scalable. It will use existing hardware infrastructure in a much more efficient manner, by utilising otherwise-unused CPU time. It will allow infohabitants to co-operate, communicate, negotiate and trade; and emergent behaviour is expected to result. It is expected that there will be an emergent economy that results from the provision and use of CPU cycles by infohabitants and their owners. The DREAM infrastructure will be evaluated with new work on distributed data mining, distributed scheduling, and the modelling of economic and social behaviour.
PEXESO Evolutionary Methods Library is the library of Evolutionary Optimization Methods for Real Domains. It is based on the original Object Oriented Algorithmic model that consists of the multi- operators technology (currently it supports 13 operator types) and "open policy" on the selection strategy (currently 4 selection strategy types). Using this method you have a possibility to compose your own optimization method using some combination of operators and selection strategies, or you can use one of 3 precomposed algorithms. It is provided with several examples and comprehensive HTML documentation.
AI::GA implements a (hopefully) generalized genetic algorithm. It does this by using an array of allowed tokens as individuals. The user has to provide a fitness function. There, the actual representation is implemented. If you have a string of chars, you can simply join them. If you want to have real numbers, you should probably use a bitwise representation and calculate the real values in your fitness function.