Paper Info
Title
Indicator-based MONEDA: A comparative study of scalability with respect to decision space dimensions
Abstract
The multi-objective neural EDA (MONEDA) was proposed with the aim of overcoming some difficulties of current MOEDAs. MONEDA has been shown to yield relevant results when confronted with complex problems. Furthermore, its performance has been shown to adequately adapt to problems with many objectives. Nevertheless, one key issue remains to be studied: MONEDA scalability with regard to the number of decision variables. In this paper has a two-fold purpose. On one hand we propose a modification of MONEDA that incorporates an indicator-based selection mechanism based on the HypE algorithm, while, on the other, we assess the indicator-based MONEDA when solving some complex two-objective problems, in particular problems UF1 to UF7 of the CEC 2009 MOP competition, configured with a progressively-increasing number of decision variables.
Year
DOI
Venue
2011
10.1109/CEC.2011.5949721
Evolutionary Computation
Keywords
Field
DocType
evolutionary computation,decision variables,hype algorithm,multiobjective neural EDA
Training set,Decision variables,Mathematical optimization,Computer science,Evolutionary computation,Artificial intelligence,Cluster analysis,Machine learning,Scalability,Complex problems
Conference
ISSN
ISBN
Citations 
Pending
978-1-4244-7834-7
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Luis Martí11007.73
Jesús García2697.62
Antonio Berlanga300.34
José M. Molina López45312.09
Molina, J.M.500.34