Chapter

Parallel Problem Solving from Nature - PPSN VIII

Volume 3242 of the series Lecture Notes in Computer Science pp 282-291

Evaluating the CMA Evolution Strategy on Multimodal Test Functions

  • Nikolaus HansenAffiliated withComputational Science and Engineering Laboratory (CSE Lab), Swiss Federal Institute of Technology (ETH) Zurich
  • , Stefan KernAffiliated withComputational Science and Engineering Laboratory (CSE Lab), Swiss Federal Institute of Technology (ETH) Zurich

* Final gross prices may vary according to local VAT.

Get Access

Abstract

In this paper the performance of the CMA evolution strategy with rank-μ-update and weighted recombination is empirically investigated on eight multimodal test functions. In particular the effect of the population size λ on the performance is investigated. Increasing the population size remarkably improves the performance on six of the eight test functions. The optimal population size takes a wide range of values, but, with one exception, scales sub-linearly with the problem dimension. The global optimum can be located in all but one function. The performance for locating the global optimum scales between linear and cubic with the problem dimension. In a comparison to state-of-the-art global search strategies the CMA evolution strategy achieves superior performance on multimodal, non-separable test functions without intricate parameter tuning.