Abstract
This paper describes a dreamy genetic algorithm scheme, emulating one basic mechanism of chronobiology: the alternation of awake and sleeping phases. We use the metaphor of the REM sleep during which the system is widely disconnected from its environment. The dream phase allows the population to reorganize and maintain a needed diversity. Experiments show that dreamy genetic algorithms improve on standard genetic algorithm, for both stationary (deceptive) and non-stationary optimization problems. A theoretical and experimental analysis suggests that dreamy genetic algorithms are better suited to complex tasks than standard genetic algorithms, due to the preservation of the population diversity.
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Escazut, C., Collard, P. (1998). Genetic algorithms at the edge of a dream. In: Hao, JK., Lutton, E., Ronald, E., Schoenauer, M., Snyers, D. (eds) Artificial Evolution. AE 1997. Lecture Notes in Computer Science, vol 1363. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0026591
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DOI: https://doi.org/10.1007/BFb0026591
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