Cellular Learning Automata (CLA) which is obtained by combining cellular automata (CA) and learning automata (LA) models is a mathematical model for dynamical complex systems that consists of a large number of simple learning components. CLA- EC, introduced recently is an evolutionary algorithm which is obtained by combining CLA and evolutionary computation (EC). In this paper CLA-EC with recombination operator is introduced. Recombination increases explorative behavior of CLA-EC and also provides a mechanism for partial structure exchange between chromosomes of population individuals that standard CLA-EC is not capable of performing it. This modification greatly improves CLA-EC ability to effectively search solution space and leave local optima. Experimental results on five optimization test functions show the superiority of this new version of CLA-EC over the standard CLA-EC.
Citation:
Borna Jafarpour, Mohammad reza Meybodi, "Recombinative CLA-EC," ictai, vol. 1, pp.415-422, 19th IEEE International Conference on Tools with Artificial Intelligence - Vol.1 (ICTAI 2007), 2007