MULTIMODAL SEARCHING TECHNIQUE BASED ON LEARNING AUTOMATA WITH CONTINUOUS INPUT AND CHANGING NUMBER OF ACTIONS

This paper describes a multimodal searching technique based on a stoch astic automaton, The environment where the automaton operates correspo nds to the function to be optimized which is assumed to be unknown fun ction of a single parameter x. The admissible region of x is quantized into N subsets, The environment response is continuous (S-model). The complete set of actions of the automaton is divided into nonempty sub sets, The action set is changing from instant to instant and is select ed based on a probability distribution, These actions are in turn asso ciated with the discrete values of the parameter x. Convergence and co nvergence rate results are presented, Simulation results illustrate th e performance of this searching technique.