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Lobachevskii Journal of Mathematics

, Volume 40, Issue 11, pp 1799–1808 | Cite as

Conformance Evaluation of Genetic Algorithm for Evolutionary Area Search of Canonical Model

  • V. K. IvanovEmail author
  • B. V. PalyukhEmail author
  • A. N. SotnikovEmail author
Article
  • 6 Downloads

Abstract

The theory and practice of genetic algorithms is largely based on the Schema Theorem. It was formulated for a canonical genetic algorithm and proves its ability to generate a sufficient number of effective schemata of individuals. Genetic algorithms to solve specific problems and to be different from canonical ones have to be checked to find out whether the Schema Theorem evaluates the algorithm fitness. The article validates the way of testing the algorithm developed as a technique of an area search. The methodology and research results are stated consistently. Coding specifics of the search queries are noted, a criterion of the coding method applicability is substantiated. A variant of the genotype geometric coding is proposed. In comparison with other methods of binary search coding, it provides a short code length and uniqueness as well as conforms the formulated criterion of applicability. Supporting experimental results are given. The Schema Theorem is shown to hold with the iterative execution of the genetic algorithm being tested.

Keywords and phrases

genetic algorithm genotype query coding crossover defining length order scheme subject search Holland’s schema theorem fitness function 

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Notes

Acknowledgments

The authors express their thanks the colleagues who have been taking part in this study, namely, P. I. Meskin and N. V. Vinogradova as well as to translator of the article O. A. Gumenyuk.

Funding

This work was done at the Tver State Technical University with supporting of the Russian Foundation of Basic Research (project no. 18-07-00358) and at the Joint Supercomputer Center of the Russian Academy of Sciences Branch of NIISI RAS within the framework of the State assignment (research topic 065-2019-0014).

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Copyright information

© Pleiades Publishing, Ltd. 2019

Authors and Affiliations

  1. 1.Tver State Technical UniversityTverRussia
  2. 2.Joint Supercomputer CentreRussian Academy of SciencesMoscowRussia

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