ESKit is a portable C library that provides implementations of some self-adaptive evolution strategies. It features a simple API, comprehensive documentation, three state of the art self-adaptive evolution strategies (Isotropic CSA-ES, CMA-ES, and Separable CMA-ES), and can optionaly uses LAPACK. The implementation strictly follows the published papers introducing those evolution strategies and performs as in the published papers. A basic benchmark program is provided.
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.