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Evol Comput. 2012 Fall;20(3):423-52. doi: 10.1162/EVCO_a_00053. Epub 2012 Jan 30.

Diagnostic assessment of search controls and failure modes in many-objective evolutionary optimization.

Author information

1
Department of Computer Science and Engineering, The Pennsylvania State University, University Park 16802, USA. dmh309@psu.edu

Abstract

The growing popularity of multiobjective evolutionary algorithms (MOEAs) for solving many-objective problems warrants the careful investigation of their search controls and failure modes. This study contributes a new diagnostic assessment framework for rigorously evaluating the effectiveness, reliability, efficiency, and controllability of MOEAs as well as identifying their search controls and failure modes. The framework is demonstrated using the recently introduced Borg MOEA, [Formula: see text]-NSGA-II, [Formula: see text]-MOEA, IBEA, OMOPSO, GDE3, MOEA/D, SPEA2, and NSGA-II on 33 instances of 18 test problems from the DTLZ, WFG, and CEC 2009 test suites. The diagnostic framework exploits Sobol's variance decomposition to provide guidance on the algorithms' non-separable, multi-parameter controls when performing a many-objective search. This study represents one of the most comprehensive empirical assessments of MOEAs ever completed.

PMID:
21970448
DOI:
10.1162/EVCO_a_00053
[Indexed for MEDLINE]

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