Do Evolutionary Algorithms Indeed Require Random Numbers? Extended Study

  • Ivan ZelinkaEmail author
  • Mohammed Chadli
  • Donald Davendra
  • Roman Senkerik
  • Michal Pluhacek
  • Jouni Lampinen
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 210)


An inherent part of evolutionary algorithms, that are based on Darwin theory of evolution and Mendel theory of genetic heritage, are random processes. In this participation, we discuss whether are random processes really needed in evolutionary algorithms. We use \(\mathcal{n}\) periodic deterministic processes instead of random number generators and compare performance of evolutionary algorithms powered by those processes and by pseudo-random number generators. Deterministic processes used in this participation are based on deterministic chaos and are used to generate periodical series with different length. Results presented here are numerical demonstration rather than mathematical proofs. We propose that a certain class of deterministic processes can be used instead of random number generators without lowering of evolutionary algorithms performance.


Particle Swarm Optimization Evolutionary Algorithm Chaotic System Random Number Generator Algorithm Performance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Ivan Zelinka
    • 1
    Email author
  • Mohammed Chadli
    • 3
  • Donald Davendra
    • 1
  • Roman Senkerik
    • 2
  • Michal Pluhacek
    • 2
  • Jouni Lampinen
    • 4
  1. 1.VSB-Technical University of OstravaOstrava-PorubaCzech Republic
  2. 2.Faculty of Applied InformaticsTomas Bata University in ZlinZlinCzech Republic
  3. 3.Laboratory of Modeling, Information and Systems (MIS)University of Picardie Jules Verne (UPJV)Amiens Cedex 1France
  4. 4.Department of Computer ScienceUniversity of VaasaVaasaFinland

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