Paper Info
Title
Are all objectives necessary? on dimensionality reduction in evolutionary multiobjective optimization
Abstract
Most of the available multiobjective evolutionary algorithms (MOEA) for approximating the Pareto set have been designed for and tested on low dimensional problems (≤3 objectives). However, it is known that problems with a high number of objectives cause additional difficulties in terms of the quality of the Pareto set approximation and running time. Furthermore, the decision making process becomes the harder the more objectives are involved. In this context, the question arises whether all objectives are necessary to preserve the problem characteristics. One may also ask under which conditions such an objective reduction is feasible, and how a minimum set of objectives can be computed. In this paper, we propose a general mathematical framework, suited to answer these three questions, and corresponding algorithms, exact and heuristic ones. The heuristic variants are geared towards direct integration into the evolutionary search process. Moreover, extensive experiments for four well-known test problems show that substantial dimensionality reductions are possible on the basis of the proposed methodology.
Year
DOI
Venue
2006
10.1007/11844297_54
PPSN
Keywords
Field
DocType
general mathematical framework,direct integration,additional difficulty,evolutionary search process,available multiobjective evolutionary algorithm,evolutionary multiobjective optimization,pareto set,extensive experiment,heuristic variant,dimensionality reduction,corresponding algorithm,minimum set,decision making process
Approximation algorithm,Mathematical optimization,Heuristic,Dimensionality reduction,Exact algorithm,Evolutionary algorithm,Computer science,Greedy algorithm,Multi-objective optimization,Pareto principle
Conference
Volume
ISSN
ISBN
4193
0302-9743
3-540-38990-3
Citations 
PageRank 
References 
96
4.11
10
Authors
2
Name
Order
Citations
PageRank
Dimo Brockhoff194853.97
Eckart Zitzler24678291.01