Genetic Algorithms and Fine-Grained Topologies for Optimization

In this paper we show how the performance of two meta-heuristic algorithms and two simple search routines varies as these algorithms are applied singly, in pairwise combinations, and in larger, finer-grained combinations. The area of application is f6 and f17, two well-known optimization benchmark problems. Our conclusion is that when these algorithms are combined in complex ways, their performance is much better than when they are used alone or in pairs, and so there is strong evidence that the current approach to optimization followed by many current practitioners with, for instance, an evolutionary algorithm succeeded by a hill-climber, could be improved on if more complex algorithm topologies were used.