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Hidden Genes Genetic Optimization for Variable-Size Design Space Problems

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  • Ossama Abdelkhalik

    (Michigan Tech University)

Abstract

This paper introduces the biologically inspired concept of hidden genes genetic algorithms; they search for optimal solutions to global optimization problems of multimodal objective functions with a variable number of design variables. A fixed chromosome length is assumed for all solutions in the population. Each chromosome is divided into effective and ineffective segments. The effective segment includes the design variables for that solution. The ineffective segment includes only hidden genes. Hidden genes are excluded in objective function evaluations. The effect of the hidden genes on the convergence of the genetic algorithm is studied. Two test cases are presented.

Suggested Citation

  • Ossama Abdelkhalik, 2013. "Hidden Genes Genetic Optimization for Variable-Size Design Space Problems," Journal of Optimization Theory and Applications, Springer, vol. 156(2), pages 450-468, February.
    • Handle: RePEc:spr:joptap:v:156:y:2013:i:2:d:10.1007_s10957-012-0122-6
    DOI: 10.1007/s10957-012-0122-6
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    References listed on IDEAS

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    1. Michael Bartholomew-Biggs, 2008. "Nonlinear Optimization with Engineering Applications," Springer Optimization and Its Applications, Springer, number 978-0-387-78723-7, September.
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    Cited by:

    1. von Schantz, Anton & Ehtamo, Harri, 2022. "Minimizing the evacuation time of a crowd from a complex building using rescue guides," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).

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