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Genetic algorithms with shrinking population size

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Abstract

A Genetic Algorithm (GA) is an evolutionary computation technique inspired by the principle of biological evolution via natural selection. It employs the fundamental components of evolution, such as selection, mating, and mutation, which continue from generation to generation, creating better solutions as time progresses. Although it is mostly used as an optimization tool, GA enjoys a wide spectrum of applications in diverse fields such as engineering, medicine, and ecology, among others. In this study, we propose three different population size reduction methods for a typical GA optimization, aiming to increase efficiency. Additionally, we compare the accuracy and precision of these methods using Monte Carlo simulations.

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Authors and Affiliations

  1. Department of Mathematics, Illinois State University, Campus Box 4520, Normal, IL, 61790-4520, USA

    Joshua W. Hallam, Olcay Akman & Füsun Akman

Authors
  1. Joshua W. Hallam
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  2. Olcay Akman
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  3. Füsun Akman
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Corresponding author

Correspondence to Olcay Akman.

Additional information

Supported by program of excellence award from Illinois State University.

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Hallam, J.W., Akman, O. & Akman, F. Genetic algorithms with shrinking population size. Comput Stat 25, 691–705 (2010). https://doi.org/10.1007/s00180-010-0197-1

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  • DOI: https://doi.org/10.1007/s00180-010-0197-1

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