Memetic algorithms

Cotta, C; Mathieson, L; Moscato, P

Memetic algorithms

Cotta, C Mathieson, L

Moscato, P

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Publication Type:: Chapter
Citation:: Handbook of Heuristics, 2018, 1-2 pp. 607 - 638
Issue Date:: 2018-08-13

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Field	Value	Language
dc.contributor.author	Cotta, C	en_US
dc.contributor.author	Mathieson, L https://orcid.org/0000-0001-6470-2296	en_US
dc.contributor.author	Moscato, P	en_US
dc.date.issued	2018-08-13	en_US
dc.identifier.citation	Handbook of Heuristics, 2018, 1-2 pp. 607 - 638	en_US
dc.identifier.isbn	9783319071237	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132083
dc.description.abstract	© Springer International Publishing AG, part of Springer Nature 2018. All rights reserved. Memetic algorithms provide one of the most effective and flexible metaheuristic approaches for tackling hard optimization problems. Memetic algorithms address the difficulty of developing high-performance universal heuristics by encouraging the exploitation of multiple heuristics acting in concert, making use of all available sources of information for a problem. This approach has resulted in a rich arsenal of heuristic algorithms and metaheuristic frameworks for many problems. This chapter discusses the philosophy of the memetic paradigm, lays out the structure of a memetic algorithm, develops several example algorithms, surveys recent work in the field, and discusses the possible future directions of memetic algorithms.	en_US
dc.relation.ispartof	Handbook of Heuristics	en_US
dc.relation.isbasedon	10.1007/978-3-319-07124-4_29	en_US
dc.title	Memetic algorithms	en_US
dc.type	Chapter
utslib.citation.volume	1-2	en_US
utslib.for	0101 Pure Mathematics	en_US
utslib.for	0102 Applied Mathematics	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/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	1-2	en_US

Abstract:

© Springer International Publishing AG, part of Springer Nature 2018. All rights reserved. Memetic algorithms provide one of the most effective and flexible metaheuristic approaches for tackling hard optimization problems. Memetic algorithms address the difficulty of developing high-performance universal heuristics by encouraging the exploitation of multiple heuristics acting in concert, making use of all available sources of information for a problem. This approach has resulted in a rich arsenal of heuristic algorithms and metaheuristic frameworks for many problems. This chapter discusses the philosophy of the memetic paradigm, lays out the structure of a memetic algorithm, develops several example algorithms, surveys recent work in the field, and discusses the possible future directions of memetic algorithms.

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http://hdl.handle.net/10453/132083