Implicit model selection based on variable transformations in estimation of distribution

Authors:
Emanuele Corsano;Davide Cucci;Luigi Malagò;Matteo Matteucci
Affiliations:
Department of Electronics and Information, Politecnico di Milano, Milano, Italy;Department of Electronics and Information, Politecnico di Milano, Milano, Italy;Department of Electronics and Information, Politecnico di Milano, Milano, Italy;Department of Electronics and Information, Politecnico di Milano, Milano, Italy
Venue:
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Year:
2012

Citing 1
Cited 1

A fully multivariate DEUM algorithm

CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation

Variable transformations in estimation of distribution algorithms

PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we address the problem of model selection in Estimation of Distribution Algorithms from a novel perspective. We perform an implicit model selection by transforming the variables and choosing a low dimensional model in the new variable space. We apply such paradigm in EDAs and we introduce a novel algorithm called I-FCA, which makes use of the independence model in the transformed space, yet being able to recover higher order interactions among the original variables. We evaluated the performance of the algorithm on well known benchmarks functions in a black-box context and compared with other popular EDAs.