xega

Implementation of a scalable, highly configurable, and e(x)tended architecture for (e)volutionary and (g)enetic (a)lgorithms. Multiple representations (binary, real-coded, permutation, and derivation-tree), a rich collection of genetic operators, as well as an extended processing pipeline are provided for genetic algorithms (Goldberg, D. E. (1989, ISBN:0-201-15767-5)), differential evolution (Price, Kenneth V., Storn, Rainer M. and Lampinen, Jouni A. (2005) doi:10.1007/3-540-31306-0), simulated annealing (Aarts, E., and Korst, J. (1989, ISBN:0-471-92146-7)), grammar-based genetic programming (Geyer-Schulz (1997, ISBN:978-3-7908-0830-X)), and grammatical evolution (Ryan, C., O'Neill, M., and Collins, J. J. (2018) doi:10.1007/978-3-319-78717-6). All algorithms reuse basic adaptive mechanisms for performance optimization. Sequential or parallel execution (on multi-core machines, local clusters, and high performance computing environments) is available for all algorithms. See https://github.com/ageyerschulz/xega/tree/main/examples/executionModel.

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