| Description: |
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>. |
