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Article type: Research Article
Authors: Rastegar, Rezaa; * | Meybodi, Mohammad Rezab | Hariri, Arashc
Affiliations: [a] Department of Mathematics, Southern Illinois University, Carbondale, IL 62901, US | [b] Department of Computer Engineering, Amirkabir University of Technology, Tehran, Iran | [c] Department of Electrical and Computer Engineering, Shahid Beheshti University, Tehran, Iran
Correspondence: [*] Corresponding author. Tel.: +1 618 529 3750; E-mail: [email protected]
Abstract: In this paper a new evolutionary algorithm, called the CLA-EC (Cellular Learning Automata Based Evolutionary Computing), is proposed. This algorithm is a combination of evolutionary algorithms and the Cellular Learning Automata (CLA). In the CLA-EC each genome string in the population is assigned to one cell of the CLA, which is equipped with a set of learning automata. Actions selected by the learning automata of a cell determine the genome string for that cell. Based on a local rule, a reinforcement signal vector is generated and given to the set of learning automata residing in the cell. Each learning automaton in the cell updates its internal structure according to a learning algorithm and the received signal vector. The processes of action selection and updating the internal structures of learning automata are repeated until a predetermined criterion is met. To show the efficiency of the proposed model, to solve several optimization problems including real valued function optimization and data clustering problems.
Keywords: Cellular learning automata, evolutionary algorithm, learning automata, cellular automata, optimization, CLA-EC
DOI: 10.3233/HIS-2006-3202
Journal: International Journal of Hybrid Intelligent Systems, vol. 3, no. 2, pp. 83-98, 2006
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