DICE: A New Family of Bivariate Estimation of Distribution Algorithms based on Dichotomised Multivariate Gaussian Distributions

Lane, Fergal and Azad, R. Muhammad Atif and Ryan, Conor (2017) DICE: A New Family of Bivariate Estimation of Distribution Algorithms based on Dichotomised Multivariate Gaussian Distributions. In: Applications of Evolutionary Computation. EvoApplications 2017. Lecture Notes in Computer Science,. Lecture Notes in Computer Science, 10199 . Springer. ISBN 978-3-319-55848-6

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Abstract

A new family of Estimation of Distribution Algorithms (EDAs)
for discrete search spaces is presented. The proposed algorithms, which
we label DICE (Discrete Correlated Estimation of distribution algorithms)
are based, like previous bivariate EDAs such as MIMIC and
BMDA, on bivariate marginal distribution models. However, bivariate
models previously used in similar discrete EDAs were only able to exploit
an O(d) subset of all the O(d2) bivariate variable dependencies
between d variables. We introduce, and utilize in DICE, a model based
on dichotomised multivariate Gaussian distributions. These models are
able to capture and make use of all O(d2) bivariate variable interactions
in binary and multary search spaces. This paper tests the performances
of these new EDA models and algorithms on a suite of challenging combinatorial optimization problems, and compares their performances to previously used discrete-space bivariate EDA models. EDAs utilizing these
new dichotomised Gaussian (DG) models exhibit significantly superior
optimization performances, with the performance gap becoming more
marked with increasing dimensionality.

Item Type:	Book Section
Identification Number:	978-3-319-55848-6
Dates:	Date Event 2017 Published
Uncontrolled Keywords:	Dichotomised Gaussian models, EDAs, Combinatorial Optimization
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Faculty of Computing, Engineering and the Built Environment Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User:	Ian Mcdonald
Date Deposited:	20 Jun 2017 09:35
Last Modified:	22 Mar 2023 12:01
URI:	https://www.open-access.bcu.ac.uk/id/eprint/4706