Parallel and distributed computing with coevolutionary algorithms

Abstract

The problem of parallel and distributed function optimization is considered. Two coevolutionary algorithms with different degrees of parallelism and different levels of a global coordination 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. The LCGA may be recommended to be used in optimization systems when high degree of parallelism is possible and non global coordination is expected while the CCGA algorithm is useful when low degree of parallelism and global coordination is acceptable.