engleski

From fitness landscape to crossover operator choice

Genetic algorithms are applied to numerous problems that demonstrate different properties. To efficiently solve these problems, during the years a significant number of variation operators have been and still are created. It is a problem by itself how to correctly choose between those operators, i.e. how to find the most suitable operator (or a set) for a given problem. In this paper we investigate the choice of the suitable crossover operator on the basis of fitness landscape. The fitness landscape can be described with a number of properties, so a thorough analysis needs to be done to find the most useful ones. To achieve that, we experiment with 24 noise-free problems and floating point encoding. The results indicate it is possible to either select a suitable operator or at least to reduce the number of adequate operators with fitness landscape properties.

Heuristic Methods ; Fitness Landscape ; Crossover Operator ; Genetic Programming ; Automatic Classification

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