Referenced Items are not available for this document.
Evolutionary Algorithms can be inefficient in optimizing problems in which fitness evaluation of candidate solutions is computationally expensive. In this paper, single and multi-objective evolutionary methods assisted by meta-models are proposed and analyzed. Meta-models are used to identify promising regions of search space in order to save evaluations of objective-functions. The meta-models are produced using regularized Radial Basis Functions networks. The study in this work shows that the method assisted by meta-modeling accelerates the convergence of the evolutionary process in mono and multi-objectives optimizations.
Published in:
Neural Networks (SBRN), 2010 Eleventh Brazilian Symposium on
Date of Conference: 23-28 Oct. 2010
- Page(s):
- 170 - 175
- ISSN :
- 1522-4899
- E-ISBN :
- 978-0-7695-4210-2
- Print ISBN:
- 978-1-4244-8391-4
- INSPEC Accession Number:
- 11835701
- Conference Location :
- Sao Paulo
- Digital Object Identifier :
- 10.1109/SBRN.2010.37
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