论文信息 - Performance evaluation of an advanced local search evolutionary algorithm

Performance evaluation of an advanced local search evolutionary algorithm

One natural question when testing performance of global optimization algorithm is: how performances compare to a restart local search algorithm. One purpose of this paper is to provide results for such comparisons. To this end, the performances of a restart (advanced) local-search strategy, the CMA-ES with small initial step-size, are investigated on the 25 functions of the CEC 2005 real-parameter optimization test suit. The second aim is to clarify the theoretical background of the performance criterion proposed to quantitatively compare the search algorithms. The theoretical analysis allows us to generalize the criterion proposed and to define a new criterion that can be applied more appropriate in a different context.

Anne Auger | Nikolaus Hansen | N. Hansen | A. Auger

[1] Nikolaus Hansen,et al. Completely Derandomized Self-Adaptation in Evolution Strategies , 2001, Evolutionary Computation.

[2] Nikolaus Hansen,et al. Invariance, Self-Adaptation and Correlated Mutations and Evolution Strategies , 2000, PPSN.

[3] Nikolaus Hansen,et al. Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[4] Jing J. Liang,et al. Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

[5] Hans-Georg Beyer,et al. Qualms Regarding the Optimality of Cumulative Path Length Control in CSA/CMA-Evolution Strategies , 2003, Evolutionary Computation.

[6] Nikolaus Hansen,et al. A restart CMA evolution strategy with increasing population size , 2005, 2005 IEEE Congress on Evolutionary Computation.

[7] Nikolaus Hansen,et al. Evaluating the CMA Evolution Strategy on Multimodal Test Functions , 2004, PPSN.