When Hillclimbers Beat Genetic Algorithms in Multimodal Optimization
This study provides insights into when simple hillclimbing strategies are more effective than complex evolutionary algorithms for multimodal optimization, which is relevant for researchers in optimization and evolutionary computation, though it is incremental in nature.
The paper investigates the performance of multistart hillclimbing versus evolutionary algorithms on a multimodal optimization problem generator, showing empirically that hillclimbers outperform evolutionary algorithms in discovering all optima when there is no structure in the local optima space.
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.