6533b82afe1ef96bd128b608
RESEARCH PRODUCT
AMaLGaM IDEAs in noiseless black-box optimization benchmarking
Jörn GrahlPeter A. N. BosmanDirk Thierenssubject
Mathematical optimizationGaussianComputer Science::Neural and Evolutionary ComputationMathematicsofComputing_NUMERICALANALYSISEvolutionary algorithmBenchmarkingEvolutionary computationsymbols.namesakeIterated functionBlack boxBenchmark (computing)symbolsIncremental build modelMathematicsdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2009-07-08 | Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers |