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RK-EDA: a novel random key based estimation of distribution algorithm.

Ayodele, Mayowa; McCall, John; Regnier-Coudert, Olivier

Authors

Mayowa Ayodele

John McCall

Olivier Regnier-Coudert



Contributors

Julia Handl
Editor

Emma Hart
Editor

Peter R. Lewis
Editor

Manuel López-Ibáñez
Editor

Gabriela Ochoa
Editor

Ben Paechter
Editor

Abstract

The challenges of solving problems naturally represented as permutations by Estimation of Distribution Algorithms (EDAs) have been a recent focus of interest in the evolutionary computation community. One of the most common alternative representations for permutation based problems is the Random Key (RK), which enables the use of continuous approaches for this problem domain. However, the use of RK in EDAs have not produced competitive results to date and more recent research on permutation based EDAs have focused on creating superior algorithms with specially adapted representations. In this paper, we present RK-EDA; a novel RK based EDA that uses a cooling scheme to balance the exploration and exploitation of a search space by controlling the variance in its probabilistic model. Unlike the general performance of RK based EDAs, RK-EDA is actually competitive with the best EDAs on common permutation test problems: Flow Shop Scheduling, Linear Ordering, Quadratic Assignment, and Travelling Salesman Problems.

Start Date Sep 17, 2016
Publication Date Sep 23, 2016
Print ISSN 0302-9743
Publisher Springer (part of Springer Nature)
Pages 849-858
Series Title Lecture notes in computer science
Series Number 9921
Series ISSN 0302-9743
ISBN 9783319458229
Institution Citation AYODELE, M., MCCALL, J. and REGNIER-COUDERT, O. 2016. RK-EDA: a novel random key based estimation of distribution algorithm. In Handl, J., Hart, E., Lewis, P.R., López-Ibáñez, M., Ochoa, G. and Paechter, B. (eds.) Parallel problem solving from natuture: proceedings of the 14th International parallel problem solving from nature conference (PPSN XIV), 17-21 September 2016, Edinburgh, UK. Lecture notes in computer science, 9921. Cham: Springer [online], pages 849-858. Available from: https://doi.org/10.1007/978-3-319-45823-6_79.
DOI https://doi.org/10.1007/978-3-319-45823-6_79
Keywords Estimation of distribution algorithm; Random key; Permutation problems; Cooling scheme; Univariate model

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