When hillclimbers beat genetic algorithms in multimodal optimization

Lobo, F.J.; Bazargani, Mosab

http://hdl.handle.net/10400.1/19254

Use this identifier to reference this record.

Name:	Description:	Size:	Format:
When Hillclimbers Beat Genetic Algorithms in Multimodal Optimization.pdf		291.63 KB	Adobe PDF	Download

Send Feedback

Authors

Lobo, F.J.

Bazargani, Mosab

Abstract(s)

This paper investigates the performance of multistart next ascent hillclimbing and well-known evolutionary algorithms incorporating diversity preservation techniques on instances of the multimodal problem generator. This generator induces a class of problems in the bitstringdomain which is interesting to study from a theoretical perspective in the context of multimodal optimization, as it is a generalization of the classical OneMax and TwoMax functions for an arbitrary number of peaks. An average-case runtime analysis for multistart next ascent hill-climbing is presented for uniformly distributed equal-height instances of this class of problems. It is shown empirically that conventional niching and mating restriction techniques incorporated in an evolutionary algorithm are not sufficient to make them competitive with the hillclimbing strategy. We conjecture the reason for this behaviour is the lack of structure in the space of local optima on instances of this problem class, which makes an optimization algorithm unable to exploit information from one optimum to infer where another optimum might be. When no such structure exist, it seems that the best strategy for discovering all optima is a brute-force one. Overall, our study gives insights with respect to the adequacy of hillclimbers and evolutionary algorithms for multimodal optimization, depending on properties of the fitness landscape.