Abstract
This paper presents a methodology for parameters optimization applied to an hybrid behavioral control architecture. The coordination between behaviors in this architecture is insured using both hierarchical and fusion action mechanisms. This global mechanism of coordination is characterized by a multitude of parameters which must be finely tuned to enhance the efficiency of the execution of cooperative tasks. The proposed parameters optimization is obtained using genetic algorithms. Appropriate genetic operators are used to manipulate real chromosomes with a normalization relationship between its genes. The validation of the results is established using a large number of simulations.
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