A DGA is a genetic algorithm with novel features: relational schemata. These structures allow a more natural expression of relations existing between loci. Indeed, schemata in standard genetic algorithms can only specify values for each locus. Relational schemata are based on the notion of duality: a schema can be represented by two strings. The intent of this paper is to show the superiority of DGAs over conventional genetic algorithms in two general areas: efficiency and rehability. Thus, we show with theoretical and experimental results, that our algorithm is faster and perform consistently. The apphcation chosen for test DGAs is the optimization of an extension of Royal Road functions we call relational landscapes.
CITATION STYLE
Collard, P., Escazut, C., & Gaspar, A. (1996). Genetic algorithms and relational landscapes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1141, pp. 472–481). Springer Verlag. https://doi.org/10.1007/3-540-61723-X_1011
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