Abstract

An important issue in multiobjective optimization is the quantitative comparison of the performance of different algorithms. In the case of multiobjective evolutionary algorithms, the outcome is usually an approximation of the Pareto-optimal set, which is denoted as an approximation set, and therefore the question arises of how to evaluate the quality of approximation sets. Most popular are methods that assign each approximation set a vector of real numbers that reflect different aspects of the quality. Sometimes, pairs of approximation sets are also considered. In this study, we provide a rigorous analysis of the limitations underlying this type of quality assessment. To this end, a mathematical framework is developed which allows one to classify and discuss existing techniques.

Keywords

Approximation algorithmMathematical optimizationSet (abstract data type)Computer scienceMulti-objective optimizationPareto principleQuality (philosophy)Evolutionary algorithmApproximation theoryMathematics

Affiliated Institutions

Related Publications

Handbook of Genetic Algorithms

Lawrence Davis

This book sets out to explain what genetic algorithms are and how they can be used to solve real-world problems. The first objective is tackled by the editor, Lawrence Davis. Th...

1991 7308 citations

Publication Info

Year
2003
Type
article
Volume
7
Issue
2
Pages
117-132
Citations
4088
Access
Closed

External Links

Download PDF (Free) View on DOI.org Semantic Scholar

Social Impact

Altmetric
PlumX Metrics

Social media, news, blog, policy document mentions

Citation Metrics

4088
OpenAlex
321
Influential
3524
CrossRef

Cite This

Eckart Zitzler, Lothar Thiele, Marco Laumanns et al. (2003). Performance assessment of multiobjective optimizers: an analysis and review. IEEE Transactions on Evolutionary Computation , 7 (2) , 117-132. https://doi.org/10.1109/tevc.2003.810758

Identifiers

DOI
10.1109/tevc.2003.810758

Data Quality

Data completeness: 81%