Optimization based on a team of automata with binary outputs

Authors:
Kaddour Najim;Alexander S. Poznyak;Enso Ikonen
Affiliations:
Process Control Laboratory, E.N.S.I.A.C.E.T., 118, route de Narbonne, F-31077 Toulouse cedex 4, France;Departamento de Control Automítico, CINVESTAV-INP, A.P. 07300, Mexico D.F., Mexico;Systems Engineering Laboratory, Department of Process and Environmental Engineering, FIN-90014 University of Oulu, Oulu, Finland
Venue:
Automatica (Journal of IFAC)
Year:
2004

Citing 2
Cited 2

A new learning algorithm for the hierarchical structure learning automata operating in the nonstationary S-model random environment

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Genetic learning automata for function optimization

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Online optimization of replacement policies using learning automata

International Journal of Systems Science
A cellular learning automata model of investment behavior in the stock market

Neurocomputing

Quantified Score

Hi-index	22.14

Visualization

Abstract

This paper presents an algorithm for optimization of a multimodal scalar-argument function based on a team of learning stochastic automata with binary actions (outputs) 0 or 1. The action of the team of automata consists of a digital number which represents the environment input. The probability distribution associated with each automaton is adjusted using a modified version of the Bush-Mosteller reinforcement scheme with a continuous environment response and a time-varying correction factor. The asymptotic properties of this optimization algorithm are presented. An example illustrates the feasibility of this optimization algorithm.