Genetic Algorithm with Modified Crossover for Grillage Optimization

  • Mikalojus Ramanauskas Vilnius Gediminas Technical University
  • Dmitrij Šešok
  • Rimantas Belevičius
  • Eugenijus Kurilovas Vilnius Gediminas Technical University; Vilnius University Institute of Mathematics and Informatics
  • Saulius Valentinavičius

Abstract

Modified genetic algorithm with special phenotypes' selection and crossover operators with default specified rules is proposed in this paper thus refusing the random crossover. The suggested crossover operator enables wide distribution of genes of the best phenotypes over the whole population. During selection and crossover, the best phenotypes of the newest population and additionally the genes of the best individuals of two previous populations are involved. The effectiveness of the modified algorithm is shown numerically on the real-life global optimization problem from civil engineering - the optimal pile placement problem under grillage-type foundations. This problem is a fair indicator for global optimization algorithms since the ideal solutions are known in advance but with unknown magnitudes of design parameters. Comparison of the proposed algorithm with 6 other stochastic optimization algorithms clearly reveals its advantages: at similar accuracy level the algorithm requires less time for tuning of genetic parameters and provides narrower confidence intervals on the results than other algorithms.

Author Biography

Eugenijus Kurilovas, Vilnius Gediminas Technical University; Vilnius University Institute of Mathematics and Informatics
Department of Information Technologies

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Published
2017-04-23
How to Cite
RAMANAUSKAS, Mikalojus et al. Genetic Algorithm with Modified Crossover for Grillage Optimization. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 12, n. 3, p. 393-401, apr. 2017. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/2813>. Date accessed: 08 july 2020. doi: https://doi.org/10.15837/ijccc.2017.3.2813.

Keywords

genetic algorithm; crossover operator; grillage optimization