Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
An experimental investigation of model-based parameter optimisation: SPO and beyond
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Improvement strategies for the F-Race algorithm: sampling design and iterative refinement
HM'07 Proceedings of the 4th international conference on Hybrid metaheuristics
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Anytime algorithms aim to produce a high-quality solution for any termination criterion. A recent proposal is to improve automatically the anytime behavior of single-objective optimization algorithms by incorporating the hypervolume, a well-known quality measure in multi-objective optimization, into an automatic configuration tool. In this paper, we show that the anytime behavior of IPOP-CMA-ES can be significantly improved with respect to its default parameters by applying this method. We also show that tuning IPOP-CMA-ES with respect to the final quality obtained after a large termination criterion leads to better results at that particular termination criterion, but worsens the performance of IPOP-CMA-ES when stopped earlier. The main conclusion is that IPOP-CMA-ES should be tuned with respect to the anytime behavior if the exact termination criterion is not known in advance.