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2003

Function Optimization with Coevolutionary Algorithms

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Function Optimization with Coevolutionary Algorithms
Abstract. The problem of parallel and distributed function optimization with coevolutionary algorithms is considered. Two coevolutionary algorithms are used for this purpose and compared with sequential genetic algorithm (GA). The first coevolutionary algorithm called a loosely coupled genetic algorithm (LCGA) represents a competitive coevolutionary approach to problem solving and is compared with another coevolutionary algoritm called cooperative coevolutionary genetic algorithm (CCGA). The algorithms are applied for parallel and distributed optimization of a number of test functions known in the area of evolutionary computation. We show that both coevolutionary algorithms outperform a sequential GA. While both LCGA and CCGA algorithms offer high quality solutions, they may compete to outperform each other in some specific test optimization problems.
Franciszek Seredynski, Albert Y. Zomaya, Pascal Bo
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where IIS
Authors Franciszek Seredynski, Albert Y. Zomaya, Pascal Bouvry
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