Evolutionary Parameters Optimization for an Hybrid Control Architecture of Multicriteria Tasks

This paper presents a methodology for the determination of optimal parameters for a hybrid behavioral control architecture, which uses both, subsumption and schema motor principles, in order to achieve complex and multicriteria cooperative tasks. The proposed architecture of control is constituted by a set of independent and elementary behaviors organized in layers of skills. Specific low-level behaviors, called altruistic behaviors inspired by societies of insects (attractive or repulsive signals), are used to improve the efficiency of the control to perform cooperative box-pushing task. The parameters optimization are obtained using genetic algorithms. Appropriate genetic operators are used to manipulate real chromosomes which are constrained to have its genes with specific values, this constraint is dictated by the proposed control architecture. The validation of the results of optimization is established using a large number of simulations