Paper Info
Title
Approximation-guided evolutionary multi-objective optimization
Abstract
Multi-objective optimization problems arise frequently in applications but can often only be solved approximately by heuristic approaches. Evolutionary algorithms have been widely used to tackle multi-objective problems. These algorithms use different measures to ensure diversity in the objective space but are not guided by a formal notion of approximation. We present a new framework of an evolutionary algorithm for multi-objective optimization that allows to work with a formal notion of approximation. Our experimental results show that our approach outperforms state-of-the-art evolutionary algorithms in terms of the quality of the approximation that is obtained in particular for problems with many objectives.
Year
DOI
Venue
2011
10.5591/978-1-57735-516-8/IJCAI11-204
IJCAI
Keywords
Field
DocType
different measure,multi-objective optimization,new framework,evolutionary algorithm,heuristic approach,state-of-the-art evolutionary algorithm,multi-objective optimization problem,approximation-guided evolutionary multi-objective optimization,multi-objective problem,formal notion
Approximation algorithm,Heuristic,Mathematical optimization,Evolutionary algorithm,Computer science,L-reduction,Evolutionary computation,Multi-objective optimization,Evolutionary programming,Optimization problem
Conference
Citations 
PageRank 
References 
34
1.12
16
Authors
4
Name
Order
Citations
PageRank
Karl Bringmann142730.13
Tobias Friedrich245723.56
Frank Neumann31139.85
Markus Wagner435843.21