Simple taxonomy of the genetic global optimization
Keywords:
genetic algorithms, stochastic search, two-phase strategiesAbstract
The paper tries to show the role that can be played by genetic optimization strategies in solving huge global optimization problems in computational mechanics and other branches of high technology. Genetic algorithms are especially recommended as the first phase in two-phase stochastic optimization. The self-adaptability of genetic search is shown on the basis of the mathematical model introduced by M. Vose. Main goals of adaptation are used as leading criteria in the simple taxonomy of genetic strategies.
References
[1] R.W. Anderson. The Baldwin effect. C3.4.l. in (42).
[2] J. Arabas, Z. Michalewicz, J . Mulawka. GAVaPS - a genetic algorithm with varying population size. Proc. Of the 1st IEEE Conf. on Evolutionary Computation, Orlando, FL, June 1994, pp. 73-78. Piscataway, NJ, IEEE, 1994.
[3] J . Arabas. Lectures on Evolutionary Al90rithms (in Polish). WNT, 200l.
[4] T. Back. Mutation parameters. El.2 in (6).
[5] T. Back. Self-adaptation. C7.1 in (6).
[2] J. Arabas, Z. Michalewicz, J . Mulawka. GAVaPS - a genetic algorithm with varying population size. Proc. Of the 1st IEEE Conf. on Evolutionary Computation, Orlando, FL, June 1994, pp. 73-78. Piscataway, NJ, IEEE, 1994.
[3] J . Arabas. Lectures on Evolutionary Al90rithms (in Polish). WNT, 200l.
[4] T. Back. Mutation parameters. El.2 in (6).
[5] T. Back. Self-adaptation. C7.1 in (6).
Published
2023-02-24
Issue
pp. 135-149
Section
Articles
License
Copyright © The Author(s).
This work is licensed under the Creative Commons Attribution 4.0 International CC BY 4.0.
How to Cite
Schaefer, R. (2023). Simple taxonomy of the genetic global optimization. Computer Assisted Methods in Engineering and Science, 9(1), 135-149. https://cames3.ippt.pan.pl/index.php/cames/article/view/1151
