| A ripple-spreading genetic algorithm for the aircraft sequencing problem. | |
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MedLine Citation:
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PMID: 20807081 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
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Abstract When genetic algorithms (GAs) are applied to combinatorial problems, permutation representations are usually adopted. As a result, such GAs are often confronted with feasibility and memory-efficiency problems. With the aircraft sequencing problem (ASP) as a study case, this paper reports on a novel binary-representation-based GA scheme for combinatorial problems. Unlike existing GAs for the ASP, which typically use permutation representations based on aircraft landing order, the new GA introduces a novel ripple-spreading model which transforms the original landing-order-based ASP solutions into value-based ones. In the new scheme, arriving aircraft are projected as points into an artificial space. A deterministic method inspired by the natural phenomenon of ripple-spreading on liquid surfaces is developed, which uses a few parameters as input to connect points on this space to form a landing sequence. A traditional GA, free of feasibility and memory-efficiency problems, can then be used to evolve the ripple-spreading related parameters in order to find an optimal sequence. Since the ripple-spreading model is the centerpiece of the new algorithm, it is called the ripple-spreading GA (RSGA). The advantages of the proposed RSGA are illustrated by extensive comparative studies for the case of the ASP. |
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Authors:
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Xiao-Bing Hu; Ezequiel A Di Paolo |
Publication Detail:
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Type: Journal Article Date: 2010-08-31 |
Journal Detail:
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Title: Evolutionary computation Volume: 19 ISSN: 1530-9304 ISO Abbreviation: Evol Comput Publication Date: 2011 |
Date Detail:
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Created Date: 2011-02-07 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9513581 Medline TA: Evol Comput Country: United States |
Other Details:
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Languages: eng Pagination: 77-106 Citation Subset: IM |
Affiliation:
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School of Engineering, University of Warwick, Coventry, CV4 7AL, United Kingdom. xiaobing.hu@warwick.ac.uk. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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