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Research Article · Open access · Published 7 January 2016

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

Siew Mooi Lim1, Md. Nasir Sulaiman1, Norwati Mustapha1 & Abu Bakar Md. Sultan1

Journal of Computer Science, Volume 11, Issue 11, pp. 1025–1031 · doi.org/10.3844/jcssp.2015.1025.1031

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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.

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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

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Parameter Settings for New Generational Genetic Algorithms for Solving Global Optimization ProblemsJournal of Computer Science · Volume 11, Issue 11 · pp. 1025–1031 · CC BY 3.0Download