Title
A comparison of global search algorithms for continuous black box optimization.
Abstract
Four methods for global numerical black box optimization with origins in the mathematical programming community are described and experimentally compared with the state of the art evolutionary method, BIPOP-CMA-ES. The methods chosen for the comparison exhibit various features that are potentially interesting for the evolutionary computation community: systematic sampling of the search space (DIRECT, MCS) possibly combined with a local search method (MCS), or a multi-start approach (NEWUOA, GLOBAL) possibly equipped with a careful selection of points to run a local optimizer from (GLOBAL). The recently proposed "comparing continuous optimizers" (COCO) methodology was adopted as the basis for the comparison. Based on the results, we draw suggestions about which algorithm should be used depending on the available budget of function evaluations, and we propose several possibilities for hybridizing evolutionary algorithms (EAs) with features of the other compared algorithms.
Year
DOI
Venue
2012
10.1162/EVCO_a_00084
Evolutionary Computation
Keywords
Field
DocType
available budget,global search algorithm,evolutionary computation community,search space direct,mathematical programming community,local search method,art evolutionary method,local optimizer,coco methodology,comparison exhibit various feature,continuous black box optimization,evolutionary algorithms eas,benchmarking
Search algorithm,Evolutionary algorithm,Multi-objective optimization,Artificial intelligence,Metaheuristic,Continuous optimization,Mathematical optimization,Global optimization,Algorithm,Evolutionary computation,Local search (optimization),Mathematics,Machine learning
Journal
Volume
Issue
ISSN
20
4
1530-9304
Citations 
PageRank 
References 
19
1.14
11
Authors
3
Name
Order
Citations
PageRank
Petr Pošík121015.44
Waltraud Huyer220620.10
László Pál3594.78