Paper Info
Title
Comparison between Pure and Surrogate-assisted Evolutionary Algorithms for Multiobjective Optimization.
Abstract
In this paper, a comparison between a "pure" genetic algorithm (GeDEA-II) and a surrogate-assisted algorithm (ASEMOO) is carried out using up-to-date multiobjective and multidimensional test functions. The experimental results show that the use of surrogates greatly improves convergence when both two-and three-objective test cases are dealt with. However, its convergence capabilities depend on how the surrogate can have an accurate picture of the fitness function landscape and seem to decrease as the number of the objective increases from two to three. On the other hand, a pure genetic algorithm always assures a minimum level of "front coverage", regardless of the problem on hand. Such minimum level could be considered sufficient for real-life problem optimizations. Also The dimensionality of the design space affects in opposite directions the two algorithms: for ASEMOO the increase of dimensionality is detrimental on performance, while GeDEA-II experiences benefits due to total amount of direct evaluations. It seems that GeDEA-II has an optimal population size around 20, regardless the dimensionality of the problem at hand.
Year
DOI
Venue
2015
10.3233/978-1-61499-619-4-229
Frontiers in Artificial Intelligence and Applications
Keywords
Field
DocType
Multiobjective Evolutionary Algorithms,Nature inspired computing formatting,Surrogates,Optimization
Mathematical optimization,Evolutionary algorithm,Computer science,Evolutionary computation,Multi-objective optimization
Conference
Volume
ISSN
Citations 
281
0922-6389
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Ernesto Benini1915.80
Giovanni Venturelli240.79
łukasz łaniewskiwollk3122.06