Paper Info
Title
A taxonomy of online stopping criteria for multi-objective evolutionary algorithms
Abstract
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.
Year
DOI
Venue
2011
10.1007/978-3-642-19893-9_2
EMO
Keywords
Field
DocType
formal guideline,intensive research,new osc approach,comprehensive matlab toolbox,matlab implementation,formal taxonomy,multi-objective evolutionary algorithm,formal structure,important research area,systematic research,formal convergence,multi objective optimization,performance indicators
Convergence (routing),Data mining,Heuristic,Performance indicator,MATLAB,Evolutionary algorithm,Matlab toolbox,Multi-objective optimization,Artificial intelligence,Machine learning,Mathematics
Conference
Volume
ISSN
Citations 
6576
0302-9743
16
PageRank 
References 
Authors
0.66
17
3
Name
Order
Citations
PageRank
Tobias Wagner11379.96
Heike Trautmann262343.22
Luis Martí31007.73