Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/70415
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Type: Conference paper
Title: Estimating the reproductive potential of offspring in evolutionary heuristics for combinatorial optimization problems
Author: Parkinson, E.
Ghandar, A.
Michalewicz, Z.
Tuson, A.
Citation: Proceedings of the 2011 IEEE Congress of Evolutionary Computation (CEC 2011), held in New Orleans, LA, USA, 5-8 June, 2011: pp.172-178
Publisher: IEEE
Publisher Place: USA
Issue Date: 2011
Series/Report no.: IEEE Congress on Evolutionary Computation
ISBN: 9781424478354
Conference Name: IEEE Congress of Evolutionary Computation (2011 : New Orleans, USA)
Statement of
Responsibility: 
Eddy Parkinson, Adam Ghandar, Zbigniew Michalewicz and Andrew Tuson
Abstract: This paper proposes a metaheuristic selection technique for controlling the progress of an evolutionary algorithm (and possibly other heuristic search techniques) to manipulate and make use of the relationship between runtime and solution quality. The paper examines the idea that very rapid increases in initial fitness may lead to premature convergence and a reported solution that is less than optimal. We examine the advantages provided by this metaheuristic selection technique in solving two different combinatorial optimization problems: including a "toy" problem of finding magic squares and a more realistic vehicle routing problem (VRP) benchmark. The method is found to be useful for finding both higher quality solutions with a marginally longer algorithm run time and for obtaining lower quality solutions in a shorter time. Furthermore, the impact on the search results is similar for both the magic square and the VRP problem providing evidence the method is scalable to other problem domains, and therefore is potentially a relatively straight forward addition to many heuristic approaches that can add value by improving both runtime and solution quality.
Rights: ©2011 IEEE
RMID: 0020115423
DOI: 10.1109/CEC.2011.5949615
Description (link): http://cec2011.org/
Published version: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5936494
Appears in Collections:Computer Science publications

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