Genetic algorithm formulation and tuning with use of test functions
Neural and Evolutionary Computing
2022-10-10 v1
Abstract
This work discusses single-objective constrained genetic algorithm with floating-point, integer, binary and permutation representation. Floating-point genetic algorithm tuning with use of test functions is done and leads to a parameterization with comparatively outstanding performance.
Cite
@article{arxiv.2210.03217,
title = {Genetic algorithm formulation and tuning with use of test functions},
author = {Tomasz Tarkowski},
journal= {arXiv preprint arXiv:2210.03217},
year = {2022}
}
Comments
10 pages, 1 figure, 2 tables. For associated software repository, see https://github.com/ttarkowski/quile