AMaLGaM IDEAs in noiseless black-box optimization benchmarking

ABSTRACT

This paper describes the application of a Gaussian Estimation-of-Distribution (EDA) for real-valued optimization to the noiseless part of a benchmark introduced in 2009 called BBOB (Black-Box Optimization Benchmarking). Specifically, the EDA considered here is the recently introduced parameter-free version of the Adapted Maximum-Likelihood Gaussian Model Iterated Density-Estimation Evolutionary Algorithm (AMaLGaM-IDEA). Also the version with incremental model building (iAMaLGaM-IDEA) is considered.

REFERENCES

• 1: Peter A. Bosman , Jörn Grahl , Dirk Thierens, Enhancing the Performance of Maximum---Likelihood Gaussian EDAs Using Anticipated Mean Shift, Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X, September 13-17, 2008, Dortmund, Germany [doi>10.1007/978-3-540-87700-4_14]
• 2: P. A. N. Bosman, J. Grahl, and D. Thierens. A parameter-free Gaussian EDA called AMaLGaM-IDEA: algorithms and benchmarks. CWI technical report (To Appear), 2009.
• 3: Peter A.N. Bosman, On empirical memory design, faster selection of bayesian factorizations and parameter-free gaussian EDAs, Proceedings of the 11th Annual conference on Genetic and evolutionary computation, July 08-12, 2009, Montreal, Québec, Canada [doi>10.1145/1569901.1569956]
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• 7: Jose A. Lozano , Pedro Larrañaga , Iñaki Inza , Endika Bengoetxea, Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing), Springer-Verlag New York, Inc., Secaucus, NJ, 2006
• 8: M. Pelikan, K. Sastry, and E. Cantu-Paz. Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications. Springer-Verlag, Berlin, 2006. In {4, 6} have been conducted using the provided C-code.

INDEX TERMS

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

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

  G. Mathematics of Computing
  G.1 NUMERICAL ANALYSIS
      G.1.6 Optimization
          Subjects: Unconstrained optimization

General Terms:
Algorithms

Keywords:
benchmarking, black-box optimization, evolutionary computation