Benchmarking a BI-population CMA-ES on the BBOB-2009 noisy testbed

ABSTRACT

We benchmark the BI-population CMA-ES on the BBOB-2009 noisy functions testbed. BI-population refers to a multistart strategy with equal budgets for two interlaced restart strategies, one with an increasing population size and one with varying small population sizes. The latter is presumably of little use on a noisy testbed. The BI-population CMA-ES could solve 29, 27 and 26 out of 30 functions in search space dimension 5, 10 and 20 respectively. The time to find the solution ranges between 100 D and 10⁵ D² objective function evaluations, where D is the search space dimension.

REFERENCES

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• 3: Nikolaus Hansen, Benchmarking a BI-population CMA-ES on the BBOB-2009 function testbed, Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference, July 08-12, 2009, Montreal, Québec, Canada [doi>10.1145/1570256.1570333]
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INDEX TERMS

Primary Classification:
  G. Mathematics of Computing
  G.1 NUMERICAL ANALYSIS
      G.1.6 Optimization

Additional Classification:
  F. Theory of Computation
  F.2 ANALYSIS OF ALGORITHMS AND PROBLEM COMPLEXITY
      F.2.1 Numerical Algorithms and Problems

General Terms:
Algorithms

Keywords:
CMA-ES, benchmarking, black-box optimization, direct search, evolutionary computation