Paper Info
Title
Critical Issues in Model-Based Surrogate Functions in Estimation of Distribution Algorithms.
Abstract
In many optimization domains the solution of the problem can be made more efficient by the construction of a surrogate fitness model. Estimation of distribution algorithms (EDAs) are a class of evolutionary algorithms particularly suitable for the conception of model-based surrogate techniques. Since EDAs generate probabilistic models, it is natural to use these models as surrogates. However, there exist many types of models and methods to learn them. The issues involved in the conception of model-based surrogates for EDAs are various and some of them have received scarce attention in the literature. In this position paper, we propose a unified view for model-based surrogates in EDAs and identify a number of critical issues that should be dealt with in order to advance the research in this area.
Year
DOI
Venue
2013
10.1007/978-3-319-03756-1_1
Lecture Notes in Computer Science
Keywords
Field
DocType
estimation of distribution algorithms,surrogate functions,function approximation,probabilistic modeling,most probable configuration,abductive inference
EDAS,Mathematical optimization,Function approximation,Estimation of distribution algorithm,Evolutionary algorithm,Computer science,Position paper,Artificial intelligence,Abductive reasoning,Probabilistic logic,Fitness model,Machine learning
Conference
Volume
ISSN
Citations 
8298
0302-9743
0
PageRank 
References 
Authors
0.34
23
3
Name
Order
Citations
PageRank
Roberto Santana135719.04
Alexander Mendiburu235533.61
José A. Lozano32148167.25