Memetic Search with Interdomain Learning: A Realization between CVRP and CARP

Feng, L; Ong, YS; Lim, MH; Tsang, IW

Memetic Search with Interdomain Learning: A Realization between CVRP and CARP

Feng, L Ong, YS Lim, MH Tsang, IW

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Evolutionary Computation, 2015, 19 (5), pp. 644 - 658
Issue Date:: 2015-10-01

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Field	Value	Language
dc.contributor.author	Feng, L	en_US
dc.contributor.author	Ong, YS	en_US
dc.contributor.author	Lim, MH	en_US
dc.contributor.author	Tsang, IW https://orcid.org/0000-0001-8095-4637	en_US
dc.date.issued	2015-10-01	en_US
dc.identifier.citation	IEEE Transactions on Evolutionary Computation, 2015, 19 (5), pp. 644 - 658	en_US
dc.identifier.issn	1089-778X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121764
dc.description.abstract	© 1997-2012 IEEE. In recent decades, a plethora of dedicated evolutionary algorithms (EAs) have been crafted to solve domain-specific complex problems more efficiently. Many advanced EAs have relied on the incorporation of domain-specific knowledge as inductive biases that is deemed to fit the problem of interest well. As such, the embedment of domain knowledge about the underlying problem within the search algorithms is becoming an established mode of enhancing evolutionary search performance. In this paper, we present a study on evolutionary memetic computing paradigm that is capable of learning and evolving knowledge meme that traverses different but related problem domains, for greater search efficiency. Focusing on combinatorial optimization as the area of study, a realization of the proposed approach is investigated on two NP-hard problem domains (i.e., capacitated vehicle routing problem and capacitated arc routing problem). Empirical studies on well-established routing problems and their respective state-of-the-art optimization solvers are presented to study the potential benefits of leveraging knowledge memes that are learned from different but related problem domains on future evolutionary search.	en_US
dc.relation.ispartof	IEEE Transactions on Evolutionary Computation	en_US
dc.relation.isbasedon	10.1109/TEVC.2014.2362558	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Memetic Search with Interdomain Learning: A Realization between CVRP and CARP	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	19	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	19	en_US

Abstract:

© 1997-2012 IEEE. In recent decades, a plethora of dedicated evolutionary algorithms (EAs) have been crafted to solve domain-specific complex problems more efficiently. Many advanced EAs have relied on the incorporation of domain-specific knowledge as inductive biases that is deemed to fit the problem of interest well. As such, the embedment of domain knowledge about the underlying problem within the search algorithms is becoming an established mode of enhancing evolutionary search performance. In this paper, we present a study on evolutionary memetic computing paradigm that is capable of learning and evolving knowledge meme that traverses different but related problem domains, for greater search efficiency. Focusing on combinatorial optimization as the area of study, a realization of the proposed approach is investigated on two NP-hard problem domains (i.e., capacitated vehicle routing problem and capacitated arc routing problem). Empirical studies on well-established routing problems and their respective state-of-the-art optimization solvers are presented to study the potential benefits of leveraging knowledge memes that are learned from different but related problem domains on future evolutionary search.

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http://hdl.handle.net/10453/121764