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Research Article Open Access

Parameter Settings for New Generational Genetic Algorithms for Solving Global Optimization Problems

Siew Mooi Lim1, Md. Nasir Sulaiman1, Norwati Mustapha1 and Abu Bakar Md. Sultan1
  • 1 Universiti Putra Malaysia, Malaysia

Abstract

This study operates within experimental design with two main tools of Taguchi method namely orthogonal array and signal to noise ratio to discover the optimal parameter settings for newly proposed generational genetic algorithms; they are Laplace Crossover-Scale Truncated Pareto Mutation (LX-STPM) and Rayleigh Crossover-Scale Truncated Pareto Mutation (RX-STPM). It concluded that GA parameter settings are algorithms and problems dependent.

Journal of Computer Science
Volume 11 No. 11, 2015, 1025-1031

DOI: https://doi.org/10.3844/jcssp.2015.1025.1031

Submitted On: 16 June 2015 Published On: 7 January 2016

How to Cite: Lim, S. M., Sulaiman, M. N., Mustapha, N. & Sultan, A. B. M. (2015). Parameter Settings for New Generational Genetic Algorithms for Solving Global Optimization Problems. Journal of Computer Science, 11(11), 1025-1031. https://doi.org/10.3844/jcssp.2015.1025.1031

Open AccessCopyright: © 2015 Siew Mooi Lim, Md. Nasir Sulaiman, Norwati Mustapha and Abu Bakar Md. Sultan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Keywords

  • Genetic Algorithms
  • Parameter Settings
  • Taguchi Method