AMaLGaM IDEAs in noiseless black-box optimization benchmarking

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. Categories and Subject Descriptors

• Type: Conference
• Year: 2009
• Where: GECCO
• Authors: Peter A. N. Bosman, Jörn Grahl, Dirk Thierens
• Keywords: Black-Box Optimization Benchmarking, Gaussian Estimation-of-Distribution, GECCO 2009, Maximum-Likelihood Gaussian Model, Optimization