Extended Evolutionary and Genetic Algorithms
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)
), 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)), grammatical evolution
(Ryan, C., O'Neill, M., and Collins, J. J. (2018) ),
and grammatical differential evolution (O'Neill, M. and Brabazon, A. (2006) in
Arabinia, H. (2006, ISBN:978-193-241596-3).
All algorithms reuse basic adaptive mechanisms for performance optimization.
For 'xega''s architecture, see Geyer-Schulz, A. (2025) .
Sequential or parallel execution (on multi-core machines,
local clusters, and high-performance computing environments)
is available for all algorithms. See
.