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Abstract
Genetic Algorithms (GAs) are robust probabilistic algorithms for optimization, relying strongly on parallel computation. Their power comes from multi-point exploiting of the searching space, avoiding the stagnation in local optima. First we review the state of art in GA theory. Next, two illustrative original applications highlight the efficiency of GA on multi-parameter optimization tasks: on solving systems of fuzzy relational equations, and on optimizing the parameters involved in an economic forecasting task.
Text
References
Bibliographical reference
Alexandru Agapie, « Genetic Algorithms », CASYS, 7 | 2000, 35-44.
Electronic reference
Alexandru Agapie, « Genetic Algorithms », CASYS [Online], 7 | 2000, Online since 26 September 2024, connection on 27 December 2024. URL : http://popups.uliege.be/3041-539x/index.php?id=3565
Author
Alexandru Agapie
National Institute of Microtechnology, PO Box 38-160, 72225, Bucharest, Romania