AMaLGaM IDEAs in noisy black-box optimization benchmarking

ABSTRACT

This paper describes the application of a Gaussian Estimation-of-Distribution (EDA) for real-valued optimization to the noisy 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.N. Bosman , Jörn Grahl , Dirk Thierens, AMaLGaM IDEAs in noiseless black-box optimization benchmarking, Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference, July 08-12, 2009, Montreal, Québec, Canada [doi>10.1145/1570256.1570313]
• 2: S. Finck, N. Hansen, R. Ros, and A. Auger. Real-parameter black-box optimization benchmarking 2009: Presentation of the noisy functions. Technical Report 2009/20, Research Center PPE, 2009.
• 3: N. Hansen, A. Auger, S. Finck, and R. Ros. Real-parameter black-box optimization benchmarking 2009: Experimental setup. Technical Report RR-6828, INRIA, 2009.
• 4: N. Hansen, S. Finck, R. Ros, and A. Auger. Real-parameter black-box optimization benchmarking 2009: Noisy functions definitions. Technical Report RR-6829, INRIA, 2009.

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