The dANN project is a library to help facilitate artificial neural networks, artificial intelligence, and artificial genetics within other applications. It is currently written in Java, C++, and C#. However, only the Java version is currently in active development. The other versions can only be obtained from SVN. It provides a powerful interface for programs to include conventional artificial intelligence technology and artificial genetics into their code. It also acts as a testing ground for research and development of new concepts.
EO is a template-based, ANSI-C++ evolutionary computation library that helps you to write your own stochastic optimization algorithms quickly. Evolutionary algorithms form a family of algorithms inspired by the theory of evolution, and solve various problems. They evolve a set of solutions to a given problem in order to produce the best results. These are stochastic algorithms because they iteratively use random processes. The vast majority of these methods are used to solve optimization problems, and may be also called "metaheuristics". They are also ranked among computational intelligence methods, a domain close to artificial intelligence. With the help of EO, you can easily design evolutionary algorithms that will find solutions to virtually all kind of hard optimization problems, from continuous to combinatorial ones.
GRALE is a set of tools - a library and a number of accompanying applications - to study gravitational lenses. Gravitational lenses are astronomical objects so massive that their gravitational pull even deflects light rays. This can cause multiple copies of the same background object to be visible, like a cosmic mirage. The locations and shapes of these copies can provide information about the mass distribution of the gravitational lens, which GRALE can help recover using a genetic algorithm-based method. Apart from these so-called lens inversions, it's also possible to simulate gravitational lenses.
ca-ga is a toy artificial life simulation that uses genetic algorithms on large cellular automata. It uses simple but easily extended DNA that is 8k long by default, though you can take the size out to anything you have time to evolve. It sits under each cell of a 128x128 board and orders operations to transfer energy in the hopes of achieving a kill and breed. The simulation features a mutating fitness function, emergent sex, and a proof of concept real world fitness function. After enough generations, the cells or genes could achieve collectivism and organismhood, coordinating the values of the hotspots that determine board temperature in order to maintain a desired equilibrium. But maybe not. If you work in a fitness function, an optimizing problem solver results.