Extended Evolutionary and Genetic Algorithms
A constant function with a boolean grammar.
Compile a BNF.
Create a unique filename.
The problem environment lau15
Generate the problem environment EnvXOR
Problem environment for a 2-dimensional quadratic parabola
Problem environment for a 2-dimensional quadratic parabola.
Factory for configuring a gene-dependent Crossover function.
Factory for configuring a gene-dependent DecodeGene function.
Factory for configuring a gene-dependent geneMap function.
Factory for configuring a gene-dependent InitGene function.
Factory for configuring a gene-dependent Mutation function.
Factory for configuring a gene-dependent Replication function.
Package xega
Writes xega results after each iteration to a rds file.
Run an evolutionary or genetic algorithm with the same configuration a...
Run an evolutionary or genetic algorithm for a problem environment whi...
About this version.
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)), grammatical evolution (Ryan, C., O'Neill, M., and Collins, J. J. (2018) <doi:10.1007/978-3-319-78717-6>), 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) <doi:10.5445/IR/1000187255>. 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>.