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Memetic algorithms: a short introduction

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Pablo MoscatoAuthors Info & Claims

New ideas in optimization

January 1999

Pages 219 - 234

Published: 01 January 1999 Publication History

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Cecilia JRussek PJamro EDąbrowska-Boruch AWiatr K(2020)A study of the loops control for reconfigurable computing with OpenCL in the LABS local search problemInternational Journal of High Performance Computing Applications10.1177/109434201986851534:1(103-114)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1177/1094342019868515
Jakobovic DPicek SMartins MWagner MAuger AStützle T(2019)A characterisation of S-box fitness landscapes in cryptographyProceedings of the Genetic and Evolutionary Computation Conference10.1145/3321707.3321850(285-293)Online publication date: 13-Jul-2019
https://dl.acm.org/doi/10.1145/3321707.3321850
Fernández-Leiva AGutiérrez-Fuentes Á(2019)On distributed user-centric memetic algorithmsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-3049-523:12(4019-4039)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s00500-018-3049-5
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Memetic algorithms: a short introduction

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Classification of adaptive memetic algorithms: a comparative study

Adaptation of parameters and operators represents one of the recent most important and promising areas of research in evolutionary computations; it is a form of designing self-configuring algorithms that acclimatize to suit the problem in hand. Here, our ...
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A cellular genetic algorithm (CGA) is a decentralized form of GA where individuals in a population are usually arranged in a 2D grid and interactions among individuals are restricted to a set neighborhood. In this paper, we extend the notion of ...
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Coevolving memetic algorithms are a family of metaheuristic search algorithms in which a rule-based representation of local search (LS) is coadapted alongside candidate solutions within a hybrid evolutionary system. Simple versions of these systems have ...

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Information

Published In

New ideas in optimization

January 1999

493 pages

ISBN:0077095065

Editors:
David Corne
Univ. of Reading, Reading, UK
,
Marco Dorigo
Univ. Libre de Bruxelles, Brussels, Belgium
,
Fred Glover
Univ. of Colorado, Boulder
,
Dipankar Dasgupta
Univ. of Memphis, Memphis, TN
,
Pablo Moscato
Univ. of Karlsruhe, Karlsruhe, Germany
,
Riccardo Poli
Univ. of Birmingham, Birmingham, UK
,
Kenneth V. Price
Owl Circle, Vacaville, CA

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McGraw-Hill Ltd., UK

United Kingdom

Publication History

Published: 01 January 1999

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Cecilia JRussek PJamro EDąbrowska-Boruch AWiatr K(2020)A study of the loops control for reconfigurable computing with OpenCL in the LABS local search problemInternational Journal of High Performance Computing Applications10.1177/109434201986851534:1(103-114)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1177/1094342019868515
Jakobovic DPicek SMartins MWagner MAuger AStützle T(2019)A characterisation of S-box fitness landscapes in cryptographyProceedings of the Genetic and Evolutionary Computation Conference10.1145/3321707.3321850(285-293)Online publication date: 13-Jul-2019
https://dl.acm.org/doi/10.1145/3321707.3321850
Fernández-Leiva AGutiérrez-Fuentes Á(2019)On distributed user-centric memetic algorithmsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-3049-523:12(4019-4039)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s00500-018-3049-5
Nogueras RCotta C(2018)Analyzing self-? island-based memetic algorithms in heterogeneous unstable environmentsInternational Journal of High Performance Computing Applications10.1177/109434201667866532:5(676-692)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1177/1094342016678665
Rodriguez-Fernandez VGonzalez-Pardo ACamacho D(2017)Modelling Behaviour in UAV Operations Using Higher Order Double Chain Markov ModelsIEEE Computational Intelligence Magazine10.1109/MCI.2017.274273812:4(28-37)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1109/MCI.2017.2742738
Ding JL ZCheng TXu L(2017)A hybrid evolutionary approach for the single-machine total weighted tardiness problemComputers and Industrial Engineering10.1016/j.cie.2017.04.006108:C(70-80)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1016/j.cie.2017.04.006
Korczynski WByrski AKisiel-Dorohinicki M(2016)Efficient Memetic Continuous Optimization in Agent-based ComputingProcedia Computer Science10.1016/j.procs.2016.05.37480:C(845-854)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1016/j.procs.2016.05.374
Feng LTan ALim MJiang S(2016)Band selection for hyperspectral images using probabilistic memetic algorithmSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-014-1508-120:12(4685-4693)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1007/s00500-014-1508-1
Nogueras RCotta C(2015)A Study on Meme Propagation in Multimemetic AlgorithmsInternational Journal of Applied Mathematics and Computer Science10.1515/amcs-2015-003725:3(499-512)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1515/amcs-2015-0037
Zhu ZJia SHe SSun YJi ZShen L(2015)Three-dimensional Gabor feature extraction for hyperspectral imagery classification using a memetic frameworkInformation Sciences: an International Journal10.1016/j.ins.2014.11.045298:C(274-287)Online publication date: 20-Mar-2015
https://dl.acm.org/doi/10.1016/j.ins.2014.11.045
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Classification of adaptive memetic algorithms: a comparative study

Adaptive cellular memetic algorithms

Coevolving Memetic Algorithms: A Review and Progress Report

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