سال انتشار: ۱۳۸۶

محل انتشار: سومین کنفرانس بین المللی فناوری اطلاعات و دانش

تعداد صفحات: ۱۰

نویسنده(ها):

Jafarpour – Computer Engineering and Information Technology Department Amirkabir University of Technology Tehran Iran
Meybodi – Computer Engineering and Information Technology Department Amirkabir University of Technology Tehran Iran

چکیده:

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.