A taxonomy of online stopping criteria for multi-objective evolutionary algorithms

Authors:
Tobias Wagner;Heike Trautmann;Luis Martí
Affiliations:
Institute of Machining Technology (ISF), TU Dortmund, Dortmund, Germany;Department of Computational Statistics, TU Dortmund, Dortmund, Germany;Group of Applied Artificial Intelligence, Universidad Carlos III de Madrid, Madrid, Spain
Venue:
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Year:
2011

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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.