Paper Info
Title
A More Efficient Selection Scheme in iSMS-EMOA.
Abstract
In this paper, we study iSMS-EMOA, a recently proposed approach that improves the well-known S metric selection Evolutionary Multi-Objective Algorithm (SMS-EMOA). These two indicator-based multi-objective evolutionary algorithms rely on hypervolume contributions to select individuals. Here, we propose to define a probability of using a randomly selected individual within the iSMS-EMOA's selection scheme. In order to calibrate the value of such probability, we use the EVOCA tuner. Our preliminary results indicate that we are able to save up to 33% of computations of the contribution to hypervolume with respect to the original iSMS-EMOA, without any significant quality degradation in the solutions obtained. In fact, in some cases, the approach proposed here was even able to improve the quality of the solutions obtained by the original iSMS-EMOA.
Year
DOI
Venue
2014
10.1007/978-3-319-12027-0_30
ADVANCES IN ARTIFICIAL INTELLIGENCE (IBERAMIA 2014)
Keywords
Field
DocType
Multi-objective evolutionary algorithms,Tuning,Hypervolume contribution
Truncation selection,Evolutionary algorithm,Computer science,Algorithm,Calibration,Tuner,Computation
Conference
Volume
ISSN
Citations 
8864
0302-9743
0
PageRank 
References 
Authors
0.34
6
4
Name
Order
Citations
PageRank
Adriana Menchaca-Mendez1495.89
Elizabeth Montero26910.14
María Cristina Riff320023.91
C. A. Coello Coello45799427.99