Evolutionary algorithms powered by nonrandom processes

Abstract

Inherent part of evolutionary algorithms that are based on Darwin theory of evolution and Mendel theory of genetic heritage, are random processes since genetic algorithms and evolutionary strategies use. In this participation 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 n periodical deterministic processes (based on deterministic chaos principles) instead of pseudorandom number generators and compare performance of evolutionary algorithms powered by those processes and by pseudorandom number generators. Results presented here are numerical demonstration rather than mathematical proofs. We propose hypothesis that certain class of deterministic pro- cesses can be used instead of random number generators without lowering the performance of evolutionary algorithms.