Paper Info
Title
Genetic algorithm for multi-objective optimization using GDEA
Abstract
Recently, many genetic algorithms (GAs) have been developed as an approximate method to generate Pareto frontier (the set of Pareto optimal solutions) to multi-objective optimization problem. In multi-objective GAs, there are two important problems : how to assign a fitness for each individual, and how to make the diversified individuals. In order to overcome those problems, this paper suggests a new multi-objective GA using generalized data envelopment analysis (GDEA). Through numerical examples, the paper shows that the proposed method using GDEA can generate well-distributed as well as well-approximated Pareto frontiers with less number of function evaluations.
Year
DOI
Venue
2005
10.1007/11539902_49
ICNC (3)
Keywords
Field
DocType
well-approximated pareto frontier,pareto optimal solution,diversified individual,new multi-objective ga,multi-objective optimization,function evaluation,approximate method,multi-objective gas,genetic algorithm,envelopment analysis,pareto frontier,multi objective optimization,data envelope analysis
Mathematical optimization,Computer science,Multi-objective optimization,Pareto optimal,Multiobjective programming,Data envelopment analysis,Optimization problem,Pareto principle,Genetic algorithm
Conference
Volume
ISSN
ISBN
3612
0302-9743
3-540-28320-X
Citations 
PageRank 
References 
2
0.40
10
Authors
3
Name
Order
Citations
PageRank
Yeboon Yun1388.60
Min Yoon23410.38
Hirotaka Nakayama312223.18