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Soft Computing

, Volume 22, Issue 6, pp 1791–1801 | Cite as

Investigation on evolutionary algorithms powered by nonrandom processes

  • Ivan Zelinka
  • Jouni Lampinen
  • Roman Senkerik
  • Michal Pluhacek
Methodologies and Application

Abstract

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 n 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.

Keywords

Evolutionary algorithms Pseudo-random numbers Deterministic chaos Deterministic number series 

Notes

Acknowledgements

The following grants are acknowledged for the financial support provided for this research: Grant Agency of the Czech Republic GACR P103/15/06700S, Grant of SGS No. SGS 2018/177, VSB-Technical University of Ostrava, by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme Project no. LO1303 (MSMT-7778/2014), further by the European Regional Development Fund under the Project CEBIA-Tech no. CZ.1.05/2.1.00/03.0089. This work is also based upon support by COST Action CA15140, Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Ivan Zelinka
    • 1
    • 2
  • Jouni Lampinen
    • 3
  • Roman Senkerik
    • 4
  • Michal Pluhacek
    • 4
  1. 1.Modeling Evolutionary Algorithms Simulation and Artificial Intelligence, Faculty of Electrical and Electronics EngineeringTon Duc Thang UniversityHo Chi Minh CityVietnam
  2. 2.Department of Computer Science, Faculty of Electrical Engineering and Computer ScienceVSB-Technical University of OstravaOstravaCzech Republic
  3. 3.Department of Computer Science, Faculty of TechnologyUniversity of VaasaVaasaFinland
  4. 4.Department of Informatics and Artificial IntelligenceFAI, Tomas Bata Univerzity in ZlinZlinCzech Republic

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