A Taxonomy of Online Stopping Criteria for Multi-Objective Evolutionary Algorithms

The use of multi-objective evolutionary algorithms for solving black-box problems with multiple conflicting objectives has become an important research area. However, when no gradient information is available, the examination of formal convergence or optimality criteria is often impossible. Thus, so...

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Bibliographic Details
Published inEvolutionary Multi-Criterion Optimization pp. 16 - 30
Main Authors Wagner, Tobias, Trautmann, Heike, Martí, Luis
Format Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2011
SeriesLecture Notes in Computer Science
Subjects
Convergence Detection
Multi-Objective Optimization
Performance Assessment
Performance Indicators
Termination Criterion
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Summary:The use of multi-objective evolutionary algorithms for solving black-box problems with multiple conflicting objectives has become an important research area. However, when no gradient information is available, the examination of formal convergence or optimality criteria is often impossible. Thus, sophisticated heuristic online stopping criteria (OSC) have recently become subject of intensive research. In order to establish formal guidelines for a systematic research, we present a taxonomy of OSC in this paper. We integrate the known approaches within the taxonomy and discuss them by extracting their building blocks. The formal structure of the taxonomy is used as a basis for the implementation of a comprehensive MATLAB toolbox. Both contributions, the formal taxonomy and the MATLAB implementation, provide a framework for the analysis and evaluation of existing and new OSC approaches.
ISBN:3642198929
9783642198922
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-642-19893-9_2