Publication: Indicator-based MONEDA: A Comparative Study of Scalability with Respect to Decision Space Dimensions
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2011
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IEEE
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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.
Description
Proceedings of: 2011 IEEE Congress on Evolutionary Computation (CEC), New Orleans, LA, June 5-8 2011
Keywords
Evolutionary computation, Computational modeling, Multi-objetive neural EDA (MONEDA)
Bibliographic citation
2011 IEEE Congress on Evolutionary Computation (CEC), New Orleans, LA, 5-8 June 2011, pp. 957 - 964.