Peter A.N. Bosman
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.
- P. A. N. Bosman, J. Grahl, and D. Thierens. AMaLGaM IDEAs in noiseless black-box optimization benchmarking. In A. Auger et al., editors, Proceedings of the Black Box Optimization Benchmarking BBOB Workshop at the Genetic and Evolutionary Computation Conference -- GECCO-2009, New York, New York, 2009. ACM Press. (To Appear). Google Scholar
Digital Library
- 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.Google Scholar
- N. Hansen, A. Auger, S. Finck, and R. Ros. Real-parameter black-box optimization benchmarking 2009: Experimental setup. Technical Report RR-6828, INRIA, 2009.Google Scholar
- 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.Google Scholar
Index Terms
- AMaLGaM IDEAs in noisy black-box optimization benchmarking
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