Investigation on evolutionary algorithms powered by nonrandom processes

Inherent part of evolutionary algorithms that are based on Darwin’s theory of evolution and Mendel’s theory of genetic heritage, are random processes since genetic algorithms and evolutionary strategies are used. In this paper, we present extended experiments (of our previous) of selected evolutionary algorithms and test functions showing whether random processes really are needed in evolutionary algorithms. In our experiments we used differential evolution and SOMA algorithms with functions 2ndDeJong, Ackley, Griewangk, Rastrigin, SineWave and StretchedSineWave. We use (Formula presented.) periodical deterministic processes (based on deterministic chaos principles) instead of pseudo-random number generators (PRGNs) and compare performance of evolutionary algorithms powered by those processes and by PRGNs. Results presented here are numerical demonstrations rather than mathematical proofs. We propose the hypothesis that a certain class of deterministic processes can be used instead of PRGNs without lowering the performance of evolutionary algorithms. © 2015 Springer-Verlag Berlin HeidelbergGrant Agency of the Czech Republic GACR [P103/15/06700S]; Grant of SGS [SGS 2018/177]; VSB-Technical University of Ostrava; Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme Project [LO1303 (MSMT-7778/2014)]; European Regional Development Fund under the Project CEBIA-Tech [CZ.1.05/2.1.00/03.0089]; COST Action, Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO) [CA15140