Memetic algorithm - Revision history

OAbot: Open access bot: url-access updated in citation with #oabot.

2025-05-22T23:01:38Z

Open access bot: url-access updated in citation with #oabot.

Studi90: some links added

2025-01-10T10:38:51Z

some links added

← Previous revision		Revision as of 10:38, 10 January 2025
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	{{Short description\|Algorithm for searching a problem space}}		{{Short description\|Algorithm for searching a problem space}}
	{{Evolutionary algorithms}}		{{Evolutionary algorithms}}
	In [[computer science]] and [[operations research]], a '''memetic algorithm''' (MA) is an extension of an [[evolutionary algorithm]] (EA) that aims to accelerate the evolutionary search for the optimum. An EA is a [[metaheuristic]] that reproduces the basic principles of [[biological evolution]] as a [[computer algorithm]] in order to solve challenging [[Optimization problem\|optimization]] or [[planning]] tasks, at least [[Approximation\|approximately]]. An MA uses one or more suitable [[heuristic]]s or [[Local search (optimization)\|local search]] techniques to improve the quality of solutions generated by the EA and to speed up the search. The effects on the reliability of finding the global optimum depend on both the use case and the design of the MA.		In [[computer science]] and [[operations research]], a '''memetic algorithm''' (MA) is an extension of an [[evolutionary algorithm]] (EA) that aims to accelerate the evolutionary search for the [[optimum]]. An EA is a [[metaheuristic]] that reproduces the basic principles of [[biological evolution]] as a [[computer algorithm]] in order to solve challenging [[Optimization problem\|optimization]] or [[planning]] tasks, at least [[Approximation\|approximately]]. An MA uses one or more suitable [[heuristic]]s or [[Local search (optimization)\|local search]] techniques to improve the quality of solutions generated by the EA and to speed up the search. The effects on the [[Premature convergence\|reliability of finding the global optimum]] depend on both the use case and the [[Memetic algorithm#Some design notes\|design of the MA]].

	Memetic algorithms represent one of the recent growing areas of research in [[evolutionary computation]]. The term MA is now widely used as a synergy of evolutionary or any population-based approach with separate individual learning or local improvement procedures for problem search. Quite often, MAs are also referred to in the literature as Baldwinian evolutionary algorithms ~~(EAs)~~, Lamarckian EAs, cultural algorithms, or genetic local search.		Memetic algorithms represent one of the recent growing areas of research in [[evolutionary computation]]. The term MA is now widely used as a synergy of evolutionary or any population-based approach with separate individual learning or local improvement procedures for problem search. Quite often, MAs are also referred to in the literature as Baldwinian evolutionary algorithms, Lamarckian EAs, cultural algorithms, or genetic local search.

	==Introduction==		==Introduction==
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	=== Choice of Lamarckian or Baldwinian learning ===		=== Choice of Lamarckian or Baldwinian learning ===
	It is to be decided whether a found improvement is to work only by the better fitness (Baldwinian learning) or whether also the individual is adapted accordingly (lamarckian learning). In the case of an EA, this would mean an adjustment of the genotype. This question has been controversially discussed for EAs in the literature already in the 1990s, stating that the specific use case plays a major role.<ref>{{Cite journal \|last1=Gruau \|first1=Frédéric \|last2=Whitley \|first2=Darrell \|date=September 1993 \|title=Adding Learning to the Cellular Development of Neural Networks: Evolution and the Baldwin Effect \|url=https://direct.mit.edu/evco/article/1/3/213-233/1109 \|journal=Evolutionary Computation \|language=en \|volume=1 \|issue=3 \|pages=213–233 \|doi=10.1162/evco.1993.1.3.213 \|s2cid=15048360 \|issn=1063-6560}}</ref><ref>{{Citation \|last1=Orvosh \|first1=David \|last2=Davis \|first2=Lawrence \|title=Shall We Repair? Genetic Algorithms, Combinatorial Optimization, and Feasibility Constraints \|date=1993 \|work=Conf. Proc. of the 5th Int. Conf. on Genetic Algorithms (ICGA) \|pages=650 \|editor-last=Forrest \|editor-first=Stephanie \|place=San Mateo, CA, USA \|publisher=Morgan Kaufmann \|isbn=978-1-55860-299-1 \|s2cid=10098180 }}</ref><ref>{{Citation \|last1=Whitley \|first1=Darrell \|title=Lamarckian evolution, the Baldwin effect and function optimization \|date=1994 \|url=http://link.springer.com/10.1007/3-540-58484-6_245 \|work=Parallel Problem Solving from Nature — PPSN III \|volume=866 \|pages=5–15 \|editor-last=Davidor \|editor-first=Yuval \|place=Berlin, Heidelberg \|publisher=Springer Berlin Heidelberg \|doi=10.1007/3-540-58484-6_245 \|isbn=978-3-540-58484-1 \|access-date=2023-02-07 \|last2=Gordon \|first2=V. Scott \|last3=Mathias \|first3=Keith \|editor2-last=Schwefel \|editor2-first=Hans-Paul \|editor3-last=Männer \|editor3-first=Reinhard}}</ref> The background of the debate is that genome adaptation may promote [[premature convergence]]. This risk can be effectively mitigated by other measures to better balance breadth and depth searches, such as the use of [[Population model (evolutionary algorithm)\|structured populations]].<ref~~>{{Cite~~ ~~journal \|last~~=~~Jakob \|first=Wilfried \|date=September 2010 \|title=A general cost-benefit-based adaptation framework for multimeme algorithms \|url=http~~:~~//link.springer.com/10.1007/s12293-010-0040-9~~ ~~\|journal=Memetic Computing \|series=p.207 \|language=en \|volume=2 \|issue=3 \|pages=201–218 \|doi=10.1007/s12293-010-0040-9 \|s2cid=167807 \|issn=1865-9284}}<~~/~~ref~~>		It is to be decided whether a found improvement is to work only by the better fitness (Baldwinian learning) or whether also the individual is adapted accordingly (lamarckian learning). In the case of an EA, this would mean an adjustment of the genotype. This question has been controversially discussed for EAs in the literature already in the 1990s, stating that the specific use case plays a major role.<ref>{{Cite journal \|last1=Gruau \|first1=Frédéric \|last2=Whitley \|first2=Darrell \|date=September 1993 \|title=Adding Learning to the Cellular Development of Neural Networks: Evolution and the Baldwin Effect \|url=https://direct.mit.edu/evco/article/1/3/213-233/1109 \|journal=Evolutionary Computation \|language=en \|volume=1 \|issue=3 \|pages=213–233 \|doi=10.1162/evco.1993.1.3.213 \|s2cid=15048360 \|issn=1063-6560}}</ref><ref>{{Citation \|last1=Orvosh \|first1=David \|last2=Davis \|first2=Lawrence \|title=Shall We Repair? Genetic Algorithms, Combinatorial Optimization, and Feasibility Constraints \|date=1993 \|work=Conf. Proc. of the 5th Int. Conf. on Genetic Algorithms (ICGA) \|pages=650 \|editor-last=Forrest \|editor-first=Stephanie \|place=San Mateo, CA, USA \|publisher=Morgan Kaufmann \|isbn=978-1-55860-299-1 \|s2cid=10098180 }}</ref><ref>{{Citation \|last1=Whitley \|first1=Darrell \|title=Lamarckian evolution, the Baldwin effect and function optimization \|date=1994 \|url=http://link.springer.com/10.1007/3-540-58484-6_245 \|work=Parallel Problem Solving from Nature — PPSN III \|volume=866 \|pages=5–15 \|editor-last=Davidor \|editor-first=Yuval \|place=Berlin, Heidelberg \|publisher=Springer Berlin Heidelberg \|doi=10.1007/3-540-58484-6_245 \|isbn=978-3-540-58484-1 \|access-date=2023-02-07 \|last2=Gordon \|first2=V. Scott \|last3=Mathias \|first3=Keith \|editor2-last=Schwefel \|editor2-first=Hans-Paul \|editor3-last=Männer \|editor3-first=Reinhard}}</ref> The background of the debate is that genome adaptation may promote [[premature convergence]]. This risk can be effectively mitigated by other measures to better balance breadth and depth searches, such as the use of [[Population model (evolutionary algorithm)\|structured populations]].<ref name=":0" />

	==Applications==		==Applications==

Studi90: Introduction: Deletion of the assertion that MAs would generally reduce the risk of premature convergence and replacement with a suitable text.

2025-01-10T10:11:37Z

Introduction: Deletion of the assertion that MAs would generally reduce the risk of premature convergence and replacement with a suitable text.

← Previous revision		Revision as of 10:11, 10 January 2025
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	{{Short description\|Algorithm for searching a problem space}}		{{Short description\|Algorithm for searching a problem space}}
	{{Evolutionary algorithms}}		{{Evolutionary algorithms}}
	~~A '''memetic algorithm''' (MA) in~~ [[computer science]] and [[operations research]], is an extension of ~~the traditional~~ [[~~genetic~~ algorithm]] (GA) or ~~more~~ ~~general~~ [[evolutionary ~~algorithm]]~~ ~~(EA)~~. An EA is a [[metaheuristic]] that reproduces the basic principles of [[biological evolution]] as a [[computer algorithm]] in order to solve challenging [[Optimization problem\|optimization]] or [[planning]] tasks, at least [[Approximation\|approximately]]. An MA uses a suitable [[heuristic]] or [[Local search (optimization)\|local search]] ~~technique~~ to improve the quality of solutions generated by the EA and to ~~reduce~~ the ~~likelihood of [[premature convergence]]~~.~~<ref>{{cite~~ ~~journal\|title=~~ A ~~Comparison between Memetic algorithm and Genetic algorithm for~~ the ~~cryptanalysis~~ of ~~Simplified~~ ~~Data~~ ~~Encryption~~ ~~Standard~~ ~~algorithm\|author=Poonam~~ ~~Garg~~ ~~\|journal=International~~ ~~Journal~~ of ~~Network~~ ~~Security~~ & ~~Its~~ ~~Applications~~ ~~(IJNSA)\|volume=1\|issue=1\|date=April~~ ~~2009\|arxiv= 1004~~.~~0574 \|bibcode=2010arXiv1004.0574G }}</ref>~~		In [[computer science]] and [[operations research]], a '''memetic algorithm''' (MA) is an extension of an [[evolutionary algorithm]] (EA) that aims to accelerate the evolutionary search for the optimum. An EA is a [[metaheuristic]] that reproduces the basic principles of [[biological evolution]] as a [[computer algorithm]] in order to solve challenging [[Optimization problem\|optimization]] or [[planning]] tasks, at least [[Approximation\|approximately]]. An MA uses one or more suitable [[heuristic]]s or [[Local search (optimization)\|local search]] techniques to improve the quality of solutions generated by the EA and to speed up the search. The effects on the reliability of finding the global optimum depend on both the use case and the design of the MA.

	Memetic algorithms represent one of the recent growing areas of research in [[evolutionary computation]]. The term MA is now widely used as a synergy of evolutionary or any population-based approach with separate individual learning or local improvement procedures for problem search. Quite often, MAs are also referred to in the literature as Baldwinian evolutionary algorithms (EAs), Lamarckian EAs, cultural algorithms, or genetic local search.		Memetic algorithms represent one of the recent growing areas of research in [[evolutionary computation]]. The term MA is now widely used as a synergy of evolutionary or any population-based approach with separate individual learning or local improvement procedures for problem search. Quite often, MAs are also referred to in the literature as Baldwinian evolutionary algorithms (EAs), Lamarckian EAs, cultural algorithms, or genetic local search.
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	==References==		==References==
	{{reflist\|30em}}		{{reflist\|30em}}

	[[pe:الگوریتم میمتیک]]
	[[Category:Evolutionary algorithms]]		[[Category:Evolutionary algorithms]]

Studi90: Revision of the introduction with the aim of a better classification

2025-01-09T16:36:40Z

Revision of the introduction with the aim of a better classification

← Previous revision		Revision as of 16:36, 9 January 2025
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	{{Short description\|Algorithm for searching a problem space}}		{{Short description\|Algorithm for searching a problem space}}
	{{Evolutionary algorithms}}		{{Evolutionary algorithms}}
	A '''memetic algorithm''' (MA) in [[computer science]] and [[operations research]], is an extension of the traditional [[genetic algorithm]] (GA) or more general [[evolutionary algorithm]] (EA). It ~~may~~ ~~provide~~ a ~~sufficiently~~ ~~good~~ ~~solution~~ to an [[~~optimization~~ problem]]. It uses a suitable [[heuristic]] or [[Local search (optimization)\|local search]] technique to improve the quality of solutions generated by the EA and to reduce the likelihood of [[premature convergence]].<ref>{{cite journal\|title= A Comparison between Memetic algorithm and Genetic algorithm for the cryptanalysis of Simplified Data Encryption Standard algorithm\|author=Poonam Garg \|journal=International Journal of Network Security & Its Applications (IJNSA)\|volume=1\|issue=1\|date=April 2009\|arxiv= 1004.0574 \|bibcode=2010arXiv1004.0574G }}</ref>		A '''memetic algorithm''' (MA) in [[computer science]] and [[operations research]], is an extension of the traditional [[genetic algorithm]] (GA) or more general [[evolutionary algorithm]] (EA). An EA is a [[metaheuristic]] that reproduces the basic principles of [[biological evolution]] as a [[computer algorithm]] in order to solve challenging [[Optimization problem\|optimization]] or [[planning]] tasks, at least [[Approximation\|approximately]]. An MA uses a suitable [[heuristic]] or [[Local search (optimization)\|local search]] technique to improve the quality of solutions generated by the EA and to reduce the likelihood of [[premature convergence]].<ref>{{cite journal\|title= A Comparison between Memetic algorithm and Genetic algorithm for the cryptanalysis of Simplified Data Encryption Standard algorithm\|author=Poonam Garg \|journal=International Journal of Network Security & Its Applications (IJNSA)\|volume=1\|issue=1\|date=April 2009\|arxiv= 1004.0574 \|bibcode=2010arXiv1004.0574G }}</ref>

	Memetic algorithms represent one of the recent growing areas of research in [[evolutionary computation]]. The term MA is now widely used as a synergy of evolutionary or any population-based approach with separate individual learning or local improvement procedures for problem search. Quite often, MAs are also referred to in the literature as Baldwinian [[evolutionary ~~algorithm]]s~~ (EAs), Lamarckian EAs, cultural algorithms, or genetic local search.		Memetic algorithms represent one of the recent growing areas of research in [[evolutionary computation]]. The term MA is now widely used as a synergy of evolutionary or any population-based approach with separate individual learning or local improvement procedures for problem search. Quite often, MAs are also referred to in the literature as Baldwinian evolutionary algorithms (EAs), Lamarckian EAs, cultural algorithms, or genetic local search.

	==Introduction==		==Introduction==
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	Researchers have used memetic algorithms to tackle many classical [[NP (complexity)\|NP]] problems. To cite some of them: [[graph partition]]ing, [[knapsack problem\|multidimensional knapsack]], [[travelling salesman problem]], [[quadratic assignment problem]], [[set cover problem]], [[graph coloring#Algorithms\|minimal graph coloring]], [[independent set problem\|max independent set problem]], [[bin packing problem]], and [[Generalized Assignment Problem\|generalized assignment problem]].		Researchers have used memetic algorithms to tackle many classical [[NP (complexity)\|NP]] problems. To cite some of them: [[graph partition]]ing, [[knapsack problem\|multidimensional knapsack]], [[travelling salesman problem]], [[quadratic assignment problem]], [[set cover problem]], [[graph coloring#Algorithms\|minimal graph coloring]], [[independent set problem\|max independent set problem]], [[bin packing problem]], and [[Generalized Assignment Problem\|generalized assignment problem]].

	More recent applications include (but are not limited to) [[business analytics]] and [[data science]],<ref name=MAs-in-Data-Science-and-Business-Analytics> </ref> training of [[artificial neural network]]s,<ref name=training_ANN>{{cite conference \|author1=Ichimura, T. \|author2=Kuriyama, Y. \|title=Learning of neural networks with parallel hybrid GA using a royal road function\|conference=IEEE International Joint Conference on Neural Networks\|volume=2\|pages=1131–1136\|year=1998\|location=New York, NY \|doi=10.1109/IJCNN.1998.685931 }}</ref> [[pattern recognition]],<ref name=pattern_recognition>{{cite journal\|author1=Aguilar, J. \|author2=Colmenares, A. \|year=1998\|title=Resolution of pattern recognition problems using a hybrid genetic/random neural network learning algorithm\|journal=Pattern Analysis and Applications\|volume=1\|issue=1\|pages=52–61\|doi=10.1007/BF01238026\|s2cid=15803359 }}</ref> robotic [[motion planning]],<ref name=motion_planning>{{cite book\|author1=Ridao, M. \|author2=Riquelme, J. \|author3=Camacho, E. \|author4=Toro, M. \|title=Tasks and Methods in Applied Artificial Intelligence \|chapter=An evolutionary and local search algorithm for planning two manipulators motion \| year=1998\|volume=1416\| pages=105–114\|publisher=Springer-Verlag\|doi=10.1007/3-540-64574-8_396\|series=Lecture Notes in Computer Science\|isbn=978-3-540-64574-0\|citeseerx=10.1.1.324.2668 }}</ref> [[charged particle beam\|beam]] orientation,<ref name=beam_orientation>{{cite journal\|author1=Haas, O. \|author2=Burnham, K. \|author3=Mills, J. \|year=1998 \|title=Optimization of beam orientation in radiotherapy using planar geometry\|journal=Physics in Medicine and Biology\|volume=43\|issue=8\|pages=2179–2193\|doi=10.1088/0031-9155/43/8/013\|pmid=9725597\|bibcode=1998PMB....43.2179H \|s2cid=250856984 }}</ref> [[circuit design]],<ref name=circuit_design>{{cite journal\|author1=Harris, S. \|author2=Ifeachor, E. \|year=1998\|title=Automatic design of frequency sampling filters by hybrid genetic algorithm techniques\|journal=IEEE Transactions on Signal Processing \|volume=46 \|issue=12 \|pages=3304–3314 \|doi=10.1109/78.735305 \|bibcode=1998ITSP...46.3304H }}</ref> electric service restoration,<ref name=service_restoration>{{cite journal\|author1=Augugliaro, A. \|author2=Dusonchet, L. \|author3=Riva-Sanseverino, E. \|year=1998\|title=Service restoration in compensated distribution networks using a hybrid genetic algorithm\|journal=Electric Power Systems Research\|volume=46\|issue=1\|pages=59–66\|doi=10.1016/S0378-7796(98)00025-X\|bibcode=1998EPSR...46...59A }}</ref> medical [[expert system]]s,<ref name=medical_expert_system>{{cite journal\|author1=Wehrens, R. \|author2=Lucasius, C. \|author3=Buydens, L. \|author4=Kateman, G. \|year=1993\|title=HIPS, A hybrid self-adapting expert system for nuclear magnetic resonance spectrum interpretation using genetic algorithms\|journal=Analytica Chimica Acta\|volume=277\|issue=2\|pages=313–324\|doi=10.1016/0003-2670(93)80444-P\|bibcode=1993AcAC..277..313W \|hdl=2066/112321 \|s2cid=53954763 \|hdl-access=free}}</ref> [[single machine scheduling]],<ref name=single_machine_sched>{{cite conference\|author1=França, P. \|author2=Mendes, A. \|author3=Moscato, P. \|title=Memetic algorithms to minimize tardiness on a single machine with sequence-dependent setup times\|conference=Proceedings of the 5th International Conference of the Decision Sciences Institute\|pages=1708–1710\|year=1999\|location=Athens, Greece\|s2cid=10797987 }}</ref> automatic timetabling (notably, the timetable for the [[NHL]]),<ref name="nhl_timetabling">{{cite journal \| last=Costa \| first=Daniel \| title=An Evolutionary Tabu Search Algorithm And The NHL Scheduling Problem \| journal=INFOR: Information Systems and Operational Research \| volume=33 \| issue=3 \| year=1995 \| doi=10.1080/03155986.1995.11732279 \| pages=161–178\| s2cid=15491435 }}</ref> [[Schedule (workplace)\|manpower scheduling]],<ref name=nurse_rostering>{{cite conference\|author=Aickelin, U.\|title=Nurse rostering with genetic algorithms\|conference=Proceedings of young operational research conference 1998\|year=1998\|location=Guildford, UK\|arxiv=1004.2870}}</ref> [[nurse rostering problem\|nurse rostering optimisation]],<ref name=nurse_rostering_function_opt>{{cite book\| author = Ozcan, E.\|title=Practice and Theory of Automated Timetabling VI\|year=2007\|chapter=Memes, Self-generation and Nurse Rostering\|volume=3867\|pages=85–104\|publisher=Springer-Verlag\|doi=10.1007/978-3-540-77345-0_6\|series=Lecture Notes in Computer Science\|isbn=978-3-540-77344-3}}</ref> [[processor allocation]],<ref name=proc_alloc>{{cite journal\|author1=Ozcan, E. \|author2=Onbasioglu, E. \|year=2007\|title=Memetic Algorithms for Parallel Code Optimization\|journal=International Journal of Parallel Programming\|volume=35\|issue=1\|pages=33–61\|doi=10.1007/s10766-006-0026-x\|s2cid=15182941 }}</ref> maintenance scheduling (for example, of an electric distribution network),<ref name=planned_maintenance>{{cite journal\|author1=Burke, E. \|author2=Smith, A. \|year=1999\|title=A memetic algorithm to schedule planned maintenance for the national grid\| journal=Journal of Experimental Algorithmics \|issue=4\|pages=1–13 \|doi=10.1145/347792.347801 \|volume=4\|s2cid=17174080 \|doi-access=free}}</ref> [[Scheduling (production processes)\|scheduling]] of multiple [[Workflow\|workflows]] to constrained heterogeneous resources,<ref>{{Cite journal \|last1=Jakob \|first1=Wilfried \|last2=Strack \|first2=Sylvia \|last3=Quinte \|first3=Alexander \|last4=Bengel \|first4=Günther \|last5=Stucky \|first5=Karl-Uwe \|last6=Süß \|first6=Wolfgang \|date=2013-04-22 \|title=Fast Rescheduling of Multiple Workflows to Constrained Heterogeneous Resources Using Multi-Criteria Memetic Computing \|journal=Algorithms \|language=en \|volume=6 \|issue=2 \|pages=245–277 \|doi=10.3390/a6020245 \|issn=1999-4893\|doi-access=free }}</ref> multidimensional knapsack problem,<ref name=mkp_ma>{{cite journal\|author1=Ozcan, E. \|author2=Basaran, C. \|year=2009\|title=A Case Study of Memetic Algorithms for Constraint Optimization\|journal=Soft Computing: A Fusion of Foundations, Methodologies and Applications \|volume=13\|issue=8–9 \|pages=871–882 \|doi=10.1007/s00500-008-0354-4 \|citeseerx=10.1.1.368.7327 \|s2cid=17032624 }}</ref> [[VLSI]] design,<ref name="vlsi_design">{{cite journal \|author=Areibi \|first1=S. \|last2=Yang \|first2=Z. \|year=2004 \|title=Effective memetic algorithms for VLSI design automation = genetic algorithms + local search + multi-level clustering \|journal=Evolutionary Computation \|volume=12 \|issue=3 \|pages=327–353 \|doi=10.1162/1063656041774947 \|pmid=15355604 \|s2cid=2190268}}</ref> [[cluster analysis\|clustering]] of [[expression profiling\|gene expression profiles]],<ref name=clustering_gene_expression >{{cite book\|author1=Merz, P. \|author2=Zell, A. \|title = Parallel Problem Solving from Nature — PPSN VII\|volume=2439 \|year=2002\|publisher=[[Springer Science+Business Media\|Springer]]\|doi=10.1007/3-540-45712-7_78\|pages=811–820\| chapter=Clustering Gene Expression Profiles with Memetic Algorithms\|series=Lecture Notes in Computer Science \|isbn=978-3-540-44139-7 }}</ref> feature/gene selection,<ref name=gene_selection1>{{cite journal\|author=Zexuan Zhu, Y. S. Ong and M. Dash\|title=Markov Blanket-Embedded Genetic Algorithm for Gene Selection\|year=2007\|journal=Pattern Recognition\|volume=49\|issue=11\|pages=3236–3248\|doi=10.1016/j.patcog.2007.02.007\|bibcode=2007PatRe..40.3236Z}}</ref><ref name=gene_selection2>{{cite journal\|author=Zexuan Zhu, Y. S. Ong and M. Dash\|title=Wrapper-Filter Feature Selection Algorithm Using A Memetic Framework\|year=2007\|journal=IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics\|volume=37\|issue=1\|pages=70–76\|doi=10.1109/TSMCB.2006.883267\|pmid=17278560\|hdl=10338.dmlcz/141593\|s2cid=18382400\|hdl-access=free}}</ref> parameter determination for hardware fault injection,<ref>{{Cite web\|title=Artificial Intelligence for Fault Injection Parameter Selection {{!}} Marina Krček {{!}} Hardwear.io Webinar\|url=https://hardwear.io/webinar/AI-for-fault-injection-parameter-selection.php\|access-date=2021-05-21\|website=hardwear.io}}</ref> and multi-class, multi-objective [[feature selection]].<ref>{{Cite journal \|last1=Zhu \|first1=Zexuan \|last2=Ong \|first2=Yew-Soon \|last3=Zurada \|first3=Jacek M \|date=April 2010 \|title=Identification of Full and Partial Class Relevant Genes \|url=https://ieeexplore.ieee.org/document/4653480 \|journal=IEEE/ACM Transactions on Computational Biology and Bioinformatics \|volume=7 \|issue=2 \|pages=263–277 \|doi=10.1109/TCBB.2008.105 \|pmid=20431146 \|s2cid=2904028 \|issn=1545-5963}}</ref><ref name=feature_selection2>{{cite book\|author1=G. Karkavitsas \|author2=G. Tsihrintzis \|title=Intelligent Interactive Multimedia Systems and Services \|chapter=Automatic Music Genre Classification Using Hybrid Genetic Algorithms \|name-list-style=amp \|year=2011\|volume=11\|pages=323–335\|publisher=Springer\|doi=10.1007/978-3-642-22158-3_32\|series=Smart Innovation, Systems and Technologies \|isbn=978-3-642-22157-6 \|s2cid=15011089 }}</ref>		More recent applications include (but are not limited to) [[business analytics]] and [[data science]],<ref name=MAs-in-Data-Science-and-Business-Analytics> </ref> training of [[artificial neural network]]s,<ref name=training_ANN>{{cite conference \|author1=Ichimura, T. \|author2=Kuriyama, Y. \|title=Learning of neural networks with parallel hybrid GA using a royal road function\|conference=IEEE International Joint Conference on Neural Networks\|volume=2\|pages=1131–1136\|year=1998\|location=New York, NY \|doi=10.1109/IJCNN.1998.685931 }}</ref> [[pattern recognition]],<ref name=pattern_recognition>{{cite journal\|author1=Aguilar, J. \|author2=Colmenares, A. \|year=1998\|title=Resolution of pattern recognition problems using a hybrid genetic/random neural network learning algorithm\|journal=Pattern Analysis and Applications\|volume=1\|issue=1\|pages=52–61\|doi=10.1007/BF01238026\|s2cid=15803359 }}</ref> robotic [[motion planning]],<ref name=motion_planning>{{cite book\|author1=Ridao, M. \|author2=Riquelme, J. \|author3=Camacho, E. \|author4=Toro, M. \|title=Tasks and Methods in Applied Artificial Intelligence \|chapter=An evolutionary and local search algorithm for planning two manipulators motion \| year=1998\|volume=1416\| pages=105–114\|publisher=Springer-Verlag\|doi=10.1007/3-540-64574-8_396\|series=Lecture Notes in Computer Science\|isbn=978-3-540-64574-0\|citeseerx=10.1.1.324.2668 }}</ref> [[charged particle beam\|beam]] orientation,<ref name=beam_orientation>{{cite journal\|author1=Haas, O. \|author2=Burnham, K. \|author3=Mills, J. \|year=1998 \|title=Optimization of beam orientation in radiotherapy using planar geometry\|journal=Physics in Medicine and Biology\|volume=43\|issue=8\|pages=2179–2193\|doi=10.1088/0031-9155/43/8/013\|pmid=9725597\|bibcode=1998PMB....43.2179H \|s2cid=250856984 }}</ref> [[circuit design]],<ref name=circuit_design>{{cite journal\|author1=Harris, S. \|author2=Ifeachor, E. \|year=1998\|title=Automatic design of frequency sampling filters by hybrid genetic algorithm techniques\|journal=IEEE Transactions on Signal Processing \|volume=46 \|issue=12 \|pages=3304–3314 \|doi=10.1109/78.735305 \|bibcode=1998ITSP...46.3304H }}</ref> electric service restoration,<ref name=service_restoration>{{cite journal\|author1=Augugliaro, A. \|author2=Dusonchet, L. \|author3=Riva-Sanseverino, E. \|year=1998\|title=Service restoration in compensated distribution networks using a hybrid genetic algorithm\|journal=Electric Power Systems Research\|volume=46\|issue=1\|pages=59–66\|doi=10.1016/S0378-7796(98)00025-X\|bibcode=1998EPSR...46...59A }}</ref> medical [[expert system]]s,<ref name=medical_expert_system>{{cite journal\|author1=Wehrens, R. \|author2=Lucasius, C. \|author3=Buydens, L. \|author4=Kateman, G. \|year=1993\|title=HIPS, A hybrid self-adapting expert system for nuclear magnetic resonance spectrum interpretation using genetic algorithms\|journal=Analytica Chimica Acta\|volume=277\|issue=2\|pages=313–324\|doi=10.1016/0003-2670(93)80444-P\|bibcode=1993AcAC..277..313W \|hdl=2066/112321 \|s2cid=53954763 \|hdl-access=free}}</ref> [[single machine scheduling]],<ref name=single_machine_sched>{{cite conference\|author1=França, P. \|author2=Mendes, A. \|author3=Moscato, P. \|title=Memetic algorithms to minimize tardiness on a single machine with sequence-dependent setup times\|conference=Proceedings of the 5th International Conference of the Decision Sciences Institute\|pages=1708–1710\|year=1999\|location=Athens, Greece\|s2cid=10797987 }}</ref> automatic timetabling (notably, the timetable for the [[NHL]]),<ref name="nhl_timetabling">{{cite journal \| last=Costa \| first=Daniel \| title=An Evolutionary Tabu Search Algorithm And The NHL Scheduling Problem \| journal=INFOR: Information Systems and Operational Research \| volume=33 \| issue=3 \| year=1995 \| doi=10.1080/03155986.1995.11732279 \| pages=161–178\| s2cid=15491435 }}</ref> [[Schedule (workplace)\|manpower scheduling]],<ref name=nurse_rostering>{{cite conference\|author=Aickelin, U.\|title=Nurse rostering with genetic algorithms\|conference=Proceedings of young operational research conference 1998\|year=1998\|location=Guildford, UK\|arxiv=1004.2870}}</ref> [[nurse rostering problem\|nurse rostering optimisation]],<ref name=nurse_rostering_function_opt>{{cite book\| author = Ozcan, E.\|title=Practice and Theory of Automated Timetabling VI\|year=2007\|chapter=Memes, Self-generation and Nurse Rostering\|volume=3867\|pages=85–104\|publisher=Springer-Verlag\|doi=10.1007/978-3-540-77345-0_6\|series=Lecture Notes in Computer Science\|isbn=978-3-540-77344-3}}</ref> [[Scheduling (computing)#Operating system process scheduler implementations\|processor allocation]],<ref name=proc_alloc>{{cite journal\|author1=Ozcan, E. \|author2=Onbasioglu, E. \|year=2007\|title=Memetic Algorithms for Parallel Code Optimization\|journal=International Journal of Parallel Programming\|volume=35\|issue=1\|pages=33–61\|doi=10.1007/s10766-006-0026-x\|s2cid=15182941 }}</ref> maintenance scheduling (for example, of an electric distribution network),<ref name=planned_maintenance>{{cite journal\|author1=Burke, E. \|author2=Smith, A. \|year=1999\|title=A memetic algorithm to schedule planned maintenance for the national grid\| journal=Journal of Experimental Algorithmics \|issue=4\|pages=1–13 \|doi=10.1145/347792.347801 \|volume=4\|s2cid=17174080 \|doi-access=free}}</ref> [[Scheduling (production processes)\|scheduling]] of multiple [[Workflow\|workflows]] to constrained heterogeneous resources,<ref>{{Cite journal \|last1=Jakob \|first1=Wilfried \|last2=Strack \|first2=Sylvia \|last3=Quinte \|first3=Alexander \|last4=Bengel \|first4=Günther \|last5=Stucky \|first5=Karl-Uwe \|last6=Süß \|first6=Wolfgang \|date=2013-04-22 \|title=Fast Rescheduling of Multiple Workflows to Constrained Heterogeneous Resources Using Multi-Criteria Memetic Computing \|journal=Algorithms \|language=en \|volume=6 \|issue=2 \|pages=245–277 \|doi=10.3390/a6020245 \|issn=1999-4893\|doi-access=free }}</ref> multidimensional knapsack problem,<ref name=mkp_ma>{{cite journal\|author1=Ozcan, E. \|author2=Basaran, C. \|year=2009\|title=A Case Study of Memetic Algorithms for Constraint Optimization\|journal=Soft Computing: A Fusion of Foundations, Methodologies and Applications \|volume=13\|issue=8–9 \|pages=871–882 \|doi=10.1007/s00500-008-0354-4 \|citeseerx=10.1.1.368.7327 \|s2cid=17032624 }}</ref> [[VLSI]] design,<ref name="vlsi_design">{{cite journal \|author=Areibi \|first1=S. \|last2=Yang \|first2=Z. \|year=2004 \|title=Effective memetic algorithms for VLSI design automation = genetic algorithms + local search + multi-level clustering \|journal=Evolutionary Computation \|volume=12 \|issue=3 \|pages=327–353 \|doi=10.1162/1063656041774947 \|pmid=15355604 \|s2cid=2190268}}</ref> [[cluster analysis\|clustering]] of [[expression profiling\|gene expression profiles]],<ref name=clustering_gene_expression >{{cite book\|author1=Merz, P. \|author2=Zell, A. \|title = Parallel Problem Solving from Nature — PPSN VII\|volume=2439 \|year=2002\|publisher=[[Springer Science+Business Media\|Springer]]\|doi=10.1007/3-540-45712-7_78\|pages=811–820\| chapter=Clustering Gene Expression Profiles with Memetic Algorithms\|series=Lecture Notes in Computer Science \|isbn=978-3-540-44139-7 }}</ref> feature/gene selection,<ref name=gene_selection1>{{cite journal\|author=Zexuan Zhu, Y. S. Ong and M. Dash\|title=Markov Blanket-Embedded Genetic Algorithm for Gene Selection\|year=2007\|journal=Pattern Recognition\|volume=49\|issue=11\|pages=3236–3248\|doi=10.1016/j.patcog.2007.02.007\|bibcode=2007PatRe..40.3236Z}}</ref><ref name=gene_selection2>{{cite journal\|author=Zexuan Zhu, Y. S. Ong and M. Dash\|title=Wrapper-Filter Feature Selection Algorithm Using A Memetic Framework\|year=2007\|journal=IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics\|volume=37\|issue=1\|pages=70–76\|doi=10.1109/TSMCB.2006.883267\|pmid=17278560\|hdl=10338.dmlcz/141593\|s2cid=18382400\|hdl-access=free}}</ref> parameter determination for hardware fault injection,<ref>{{Cite web\|title=Artificial Intelligence for Fault Injection Parameter Selection {{!}} Marina Krček {{!}} Hardwear.io Webinar\|url=https://hardwear.io/webinar/AI-for-fault-injection-parameter-selection.php\|access-date=2021-05-21\|website=hardwear.io}}</ref> and multi-class, multi-objective [[feature selection]].<ref>{{Cite journal \|last1=Zhu \|first1=Zexuan \|last2=Ong \|first2=Yew-Soon \|last3=Zurada \|first3=Jacek M \|date=April 2010 \|title=Identification of Full and Partial Class Relevant Genes \|url=https://ieeexplore.ieee.org/document/4653480 \|journal=IEEE/ACM Transactions on Computational Biology and Bioinformatics \|volume=7 \|issue=2 \|pages=263–277 \|doi=10.1109/TCBB.2008.105 \|pmid=20431146 \|s2cid=2904028 \|issn=1545-5963}}</ref><ref name=feature_selection2>{{cite book\|author1=G. Karkavitsas \|author2=G. Tsihrintzis \|title=Intelligent Interactive Multimedia Systems and Services \|chapter=Automatic Music Genre Classification Using Hybrid Genetic Algorithms \|name-list-style=amp \|year=2011\|volume=11\|pages=323–335\|publisher=Springer\|doi=10.1007/978-3-642-22158-3_32\|series=Smart Innovation, Systems and Technologies \|isbn=978-3-642-22157-6 \|s2cid=15011089 }}</ref>

	==Recent activities in memetic algorithms==		==Recent activities in memetic algorithms==

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Added a link.

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	[[Lamarckism\|Lamarckian learning]] in this context means to update the chromosome according to the improved solution found by the individual learning step, while [[Baldwin effect\|Baldwinian learning]] leaves the chromosome unchanged and uses only the improved fitness. This pseudo code leaves open which steps are based on the fitness of the individuals and which are not. In question are the evolving of the new population and the selection of <math>\Omega_{il}</math>.		[[Lamarckism\|Lamarckian learning]] in this context means to update the chromosome according to the improved solution found by the individual learning step, while [[Baldwin effect\|Baldwinian learning]] leaves the chromosome unchanged and uses only the improved [[Fitness function\|fitness]]. This pseudo code leaves open which steps are based on the fitness of the individuals and which are not. In question are the evolving of the new population and the selection of <math>\Omega_{il}</math>.

	Since most MA implementations are based on EAs, the pseudo code of a corresponding representative of the first generation is also given here, following Krasnogor:<ref>{{cite thesis \|last=Krasnogor \|first=Natalio \|date=2002 \|title=Studies on the Theory and Design Space of Memetic Algorithms \|type=PhD \|publisher=University of the West of England \|place=Bristol, UK \|url=https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.249135 \|page=23 }}</ref>		Since most MA implementations are based on EAs, the pseudo code of a corresponding representative of the first generation is also given here, following Krasnogor:<ref>{{cite thesis \|last=Krasnogor \|first=Natalio \|date=2002 \|title=Studies on the Theory and Design Space of Memetic Algorithms \|type=PhD \|publisher=University of the West of England \|place=Bristol, UK \|url=https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.249135 \|page=23 }}</ref>

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	==References==		==References==
	{{reflist\|30em}}		{{reflist\|30em}}
			[[pe:الگوریتم میمتیک]]

	[[Category:Evolutionary algorithms]]		[[Category:Evolutionary algorithms]]

Citation bot: Altered url. URLs might have been anonymized. Added bibcode. | Use this bot. Report bugs. | Suggested by LeapTorchGear | #UCB_webform 72/114

2024-06-17T23:26:59Z

Altered url. URLs might have been anonymized. Added bibcode. | Use this bot. Report bugs. | Suggested by LeapTorchGear | #UCB_webform 72/114

← Previous revision		Revision as of 23:26, 17 June 2024
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	=== Selection of an individual learning method or meme to be used for a particular problem or individual ===		=== Selection of an individual learning method or meme to be used for a particular problem or individual ===
	In the context of continuous optimization, individual learning exists in the form of local heuristics or conventional exact enumerative methods.<ref name="schwefel1995eao">{{cite book \|author=Schwefel \|first=Hans-Paul \|url=https://www.researchgate.net/publication/~~220690578_Evolution_and_Optimum_Seeking~~ \|title=Evolution and Optimum Seeking \|publisher=Wiley \|year=1995 \|isbn=0-471-57148-2 \|location=New York \|language=en}}</ref> Examples of individual learning strategies include the [[hill climbing]], Simplex method, Newton/Quasi-Newton method, [[interior point method]]s, [[conjugate gradient method]], line search, and other local heuristics. Note that most of the common individual learning methods are deterministic.		In the context of continuous optimization, individual learning exists in the form of local heuristics or conventional exact enumerative methods.<ref name="schwefel1995eao">{{cite book \|author=Schwefel \|first=Hans-Paul \|url=https://www.researchgate.net/publication/220690578 \|title=Evolution and Optimum Seeking \|publisher=Wiley \|year=1995 \|isbn=0-471-57148-2 \|location=New York \|language=en}}</ref> Examples of individual learning strategies include the [[hill climbing]], Simplex method, Newton/Quasi-Newton method, [[interior point method]]s, [[conjugate gradient method]], line search, and other local heuristics. Note that most of the common individual learning methods are deterministic.

	In combinatorial optimization, on the other hand, individual learning methods commonly exist in the form of heuristics (which can be deterministic or stochastic) that are tailored to a specific problem of interest. Typical heuristic procedures and schemes include the k-gene exchange, edge exchange, first-improvement, and many others.		In combinatorial optimization, on the other hand, individual learning methods commonly exist in the form of heuristics (which can be deterministic or stochastic) that are tailored to a specific problem of interest. Typical heuristic procedures and schemes include the k-gene exchange, edge exchange, first-improvement, and many others.
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	Researchers have used memetic algorithms to tackle many classical [[NP (complexity)\|NP]] problems. To cite some of them: [[graph partition]]ing, [[knapsack problem\|multidimensional knapsack]], [[travelling salesman problem]], [[quadratic assignment problem]], [[set cover problem]], [[graph coloring#Algorithms\|minimal graph coloring]], [[independent set problem\|max independent set problem]], [[bin packing problem]], and [[Generalized Assignment Problem\|generalized assignment problem]].		Researchers have used memetic algorithms to tackle many classical [[NP (complexity)\|NP]] problems. To cite some of them: [[graph partition]]ing, [[knapsack problem\|multidimensional knapsack]], [[travelling salesman problem]], [[quadratic assignment problem]], [[set cover problem]], [[graph coloring#Algorithms\|minimal graph coloring]], [[independent set problem\|max independent set problem]], [[bin packing problem]], and [[Generalized Assignment Problem\|generalized assignment problem]].

	More recent applications include (but are not limited to) [[business analytics]] and [[data science]],<ref name=MAs-in-Data-Science-and-Business-Analytics> </ref> training of [[artificial neural network]]s,<ref name=training_ANN>{{cite conference \|author1=Ichimura, T. \|author2=Kuriyama, Y. \|title=Learning of neural networks with parallel hybrid GA using a royal road function\|conference=IEEE International Joint Conference on Neural Networks\|volume=2\|pages=1131–1136\|year=1998\|location=New York, NY \|doi=10.1109/IJCNN.1998.685931 }}</ref> [[pattern recognition]],<ref name=pattern_recognition>{{cite journal\|author1=Aguilar, J. \|author2=Colmenares, A. \|year=1998\|title=Resolution of pattern recognition problems using a hybrid genetic/random neural network learning algorithm\|journal=Pattern Analysis and Applications\|volume=1\|issue=1\|pages=52–61\|doi=10.1007/BF01238026\|s2cid=15803359 }}</ref> robotic [[motion planning]],<ref name=motion_planning>{{cite book\|author1=Ridao, M. \|author2=Riquelme, J. \|author3=Camacho, E. \|author4=Toro, M. \|title=Tasks and Methods in Applied Artificial Intelligence \|chapter=An evolutionary and local search algorithm for planning two manipulators motion \| year=1998\|volume=1416\| pages=105–114\|publisher=Springer-Verlag\|doi=10.1007/3-540-64574-8_396\|series=Lecture Notes in Computer Science\|isbn=978-3-540-64574-0\|citeseerx=10.1.1.324.2668 }}</ref> [[charged particle beam\|beam]] orientation,<ref name=beam_orientation>{{cite journal\|author1=Haas, O. \|author2=Burnham, K. \|author3=Mills, J. \|year=1998 \|title=Optimization of beam orientation in radiotherapy using planar geometry\|journal=Physics in Medicine and Biology\|volume=43\|issue=8\|pages=2179–2193\|doi=10.1088/0031-9155/43/8/013\|pmid=9725597\|bibcode=1998PMB....43.2179H \|s2cid=250856984 }}</ref> [[circuit design]],<ref name=circuit_design>{{cite journal\|author1=Harris, S. \|author2=Ifeachor, E. \|year=1998\|title=Automatic design of frequency sampling filters by hybrid genetic algorithm techniques\|journal=IEEE Transactions on Signal Processing \|volume=46 \|issue=12 \|pages=3304–3314 \|doi=10.1109/78.735305 \|bibcode=1998ITSP...46.3304H }}</ref> electric service restoration,<ref name=service_restoration>{{cite journal\|author1=Augugliaro, A. \|author2=Dusonchet, L. \|author3=Riva-Sanseverino, E. \|year=1998\|title=Service restoration in compensated distribution networks using a hybrid genetic algorithm\|journal=Electric Power Systems Research\|volume=46\|issue=1\|pages=59–66\|doi=10.1016/S0378-7796(98)00025-X}}</ref> medical [[expert system]]s,<ref name=medical_expert_system>{{cite journal\|author1=Wehrens, R. \|author2=Lucasius, C. \|author3=Buydens, L. \|author4=Kateman, G. \|year=1993\|title=HIPS, A hybrid self-adapting expert system for nuclear magnetic resonance spectrum interpretation using genetic algorithms\|journal=Analytica Chimica Acta\|volume=277\|issue=2\|pages=313–324\|doi=10.1016/0003-2670(93)80444-P\|hdl=2066/112321 \|s2cid=53954763 \|hdl-access=free}}</ref> [[single machine scheduling]],<ref name=single_machine_sched>{{cite conference\|author1=França, P. \|author2=Mendes, A. \|author3=Moscato, P. \|title=Memetic algorithms to minimize tardiness on a single machine with sequence-dependent setup times\|conference=Proceedings of the 5th International Conference of the Decision Sciences Institute\|pages=1708–1710\|year=1999\|location=Athens, Greece\|s2cid=10797987 }}</ref> automatic timetabling (notably, the timetable for the [[NHL]]),<ref name="nhl_timetabling">{{cite journal \| last=Costa \| first=Daniel \| title=An Evolutionary Tabu Search Algorithm And The NHL Scheduling Problem \| journal=INFOR: Information Systems and Operational Research \| volume=33 \| issue=3 \| year=1995 \| doi=10.1080/03155986.1995.11732279 \| pages=161–178\| s2cid=15491435 }}</ref> [[Schedule (workplace)\|manpower scheduling]],<ref name=nurse_rostering>{{cite conference\|author=Aickelin, U.\|title=Nurse rostering with genetic algorithms\|conference=Proceedings of young operational research conference 1998\|year=1998\|location=Guildford, UK\|arxiv=1004.2870}}</ref> [[nurse rostering problem\|nurse rostering optimisation]],<ref name=nurse_rostering_function_opt>{{cite book\| author = Ozcan, E.\|title=Practice and Theory of Automated Timetabling VI\|year=2007\|chapter=Memes, Self-generation and Nurse Rostering\|volume=3867\|pages=85–104\|publisher=Springer-Verlag\|doi=10.1007/978-3-540-77345-0_6\|series=Lecture Notes in Computer Science\|isbn=978-3-540-77344-3}}</ref> [[processor allocation]],<ref name=proc_alloc>{{cite journal\|author1=Ozcan, E. \|author2=Onbasioglu, E. \|year=2007\|title=Memetic Algorithms for Parallel Code Optimization\|journal=International Journal of Parallel Programming\|volume=35\|issue=1\|pages=33–61\|doi=10.1007/s10766-006-0026-x\|s2cid=15182941 }}</ref> maintenance scheduling (for example, of an electric distribution network),<ref name=planned_maintenance>{{cite journal\|author1=Burke, E. \|author2=Smith, A. \|year=1999\|title=A memetic algorithm to schedule planned maintenance for the national grid\| journal=Journal of Experimental Algorithmics \|issue=4\|pages=1–13 \|doi=10.1145/347792.347801 \|volume=4\|s2cid=17174080 \|doi-access=free}}</ref> [[Scheduling (production processes)\|scheduling]] of multiple [[Workflow\|workflows]] to constrained heterogeneous resources,<ref>{{Cite journal \|last1=Jakob \|first1=Wilfried \|last2=Strack \|first2=Sylvia \|last3=Quinte \|first3=Alexander \|last4=Bengel \|first4=Günther \|last5=Stucky \|first5=Karl-Uwe \|last6=Süß \|first6=Wolfgang \|date=2013-04-22 \|title=Fast Rescheduling of Multiple Workflows to Constrained Heterogeneous Resources Using Multi-Criteria Memetic Computing \|journal=Algorithms \|language=en \|volume=6 \|issue=2 \|pages=245–277 \|doi=10.3390/a6020245 \|issn=1999-4893\|doi-access=free }}</ref> multidimensional knapsack problem,<ref name=mkp_ma>{{cite journal\|author1=Ozcan, E. \|author2=Basaran, C. \|year=2009\|title=A Case Study of Memetic Algorithms for Constraint Optimization\|journal=Soft Computing: A Fusion of Foundations, Methodologies and Applications \|volume=13\|issue=8–9 \|pages=871–882 \|doi=10.1007/s00500-008-0354-4 \|citeseerx=10.1.1.368.7327 \|s2cid=17032624 }}</ref> [[VLSI]] design,<ref name="vlsi_design">{{cite journal \|author=Areibi \|first1=S. \|last2=Yang \|first2=Z. \|year=2004 \|title=Effective memetic algorithms for VLSI design automation = genetic algorithms + local search + multi-level clustering \|journal=Evolutionary Computation \|volume=12 \|issue=3 \|pages=327–353 \|doi=10.1162/1063656041774947 \|pmid=15355604 \|s2cid=2190268}}</ref> [[cluster analysis\|clustering]] of [[expression profiling\|gene expression profiles]],<ref name=clustering_gene_expression >{{cite book\|author1=Merz, P. \|author2=Zell, A. \|title = Parallel Problem Solving from Nature — PPSN VII\|volume=2439 \|year=2002\|publisher=[[Springer Science+Business Media\|Springer]]\|doi=10.1007/3-540-45712-7_78\|pages=811–820\| chapter=Clustering Gene Expression Profiles with Memetic Algorithms\|series=Lecture Notes in Computer Science \|isbn=978-3-540-44139-7 }}</ref> feature/gene selection,<ref name=gene_selection1>{{cite journal\|author=Zexuan Zhu, Y. S. Ong and M. Dash\|title=Markov Blanket-Embedded Genetic Algorithm for Gene Selection\|year=2007\|journal=Pattern Recognition\|volume=49\|issue=11\|pages=3236–3248\|doi=10.1016/j.patcog.2007.02.007\|bibcode=2007PatRe..40.3236Z}}</ref><ref name=gene_selection2>{{cite journal\|author=Zexuan Zhu, Y. S. Ong and M. Dash\|title=Wrapper-Filter Feature Selection Algorithm Using A Memetic Framework\|year=2007\|journal=IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics\|volume=37\|issue=1\|pages=70–76\|doi=10.1109/TSMCB.2006.883267\|pmid=17278560\|hdl=10338.dmlcz/141593\|s2cid=18382400\|hdl-access=free}}</ref> parameter determination for hardware fault injection,<ref>{{Cite web\|title=Artificial Intelligence for Fault Injection Parameter Selection {{!}} Marina Krček {{!}} Hardwear.io Webinar\|url=https://hardwear.io/webinar/AI-for-fault-injection-parameter-selection.php\|access-date=2021-05-21\|website=hardwear.io}}</ref> and multi-class, multi-objective [[feature selection]].<ref>{{Cite journal \|last1=Zhu \|first1=Zexuan \|last2=Ong \|first2=Yew-Soon \|last3=Zurada \|first3=Jacek M \|date=April 2010 \|title=Identification of Full and Partial Class Relevant Genes \|url=https://ieeexplore.ieee.org/document/4653480 \|journal=IEEE/ACM Transactions on Computational Biology and Bioinformatics \|volume=7 \|issue=2 \|pages=263–277 \|doi=10.1109/TCBB.2008.105 \|pmid=20431146 \|s2cid=2904028 \|issn=1545-5963}}</ref><ref name=feature_selection2>{{cite book\|author1=G. Karkavitsas \|author2=G. Tsihrintzis \|title=Intelligent Interactive Multimedia Systems and Services \|chapter=Automatic Music Genre Classification Using Hybrid Genetic Algorithms \|name-list-style=amp \|year=2011\|volume=11\|pages=323–335\|publisher=Springer\|doi=10.1007/978-3-642-22158-3_32\|series=Smart Innovation, Systems and Technologies \|isbn=978-3-642-22157-6 \|s2cid=15011089 }}</ref>		More recent applications include (but are not limited to) [[business analytics]] and [[data science]],<ref name=MAs-in-Data-Science-and-Business-Analytics> </ref> training of [[artificial neural network]]s,<ref name=training_ANN>{{cite conference \|author1=Ichimura, T. \|author2=Kuriyama, Y. \|title=Learning of neural networks with parallel hybrid GA using a royal road function\|conference=IEEE International Joint Conference on Neural Networks\|volume=2\|pages=1131–1136\|year=1998\|location=New York, NY \|doi=10.1109/IJCNN.1998.685931 }}</ref> [[pattern recognition]],<ref name=pattern_recognition>{{cite journal\|author1=Aguilar, J. \|author2=Colmenares, A. \|year=1998\|title=Resolution of pattern recognition problems using a hybrid genetic/random neural network learning algorithm\|journal=Pattern Analysis and Applications\|volume=1\|issue=1\|pages=52–61\|doi=10.1007/BF01238026\|s2cid=15803359 }}</ref> robotic [[motion planning]],<ref name=motion_planning>{{cite book\|author1=Ridao, M. \|author2=Riquelme, J. \|author3=Camacho, E. \|author4=Toro, M. \|title=Tasks and Methods in Applied Artificial Intelligence \|chapter=An evolutionary and local search algorithm for planning two manipulators motion \| year=1998\|volume=1416\| pages=105–114\|publisher=Springer-Verlag\|doi=10.1007/3-540-64574-8_396\|series=Lecture Notes in Computer Science\|isbn=978-3-540-64574-0\|citeseerx=10.1.1.324.2668 }}</ref> [[charged particle beam\|beam]] orientation,<ref name=beam_orientation>{{cite journal\|author1=Haas, O. \|author2=Burnham, K. \|author3=Mills, J. \|year=1998 \|title=Optimization of beam orientation in radiotherapy using planar geometry\|journal=Physics in Medicine and Biology\|volume=43\|issue=8\|pages=2179–2193\|doi=10.1088/0031-9155/43/8/013\|pmid=9725597\|bibcode=1998PMB....43.2179H \|s2cid=250856984 }}</ref> [[circuit design]],<ref name=circuit_design>{{cite journal\|author1=Harris, S. \|author2=Ifeachor, E. \|year=1998\|title=Automatic design of frequency sampling filters by hybrid genetic algorithm techniques\|journal=IEEE Transactions on Signal Processing \|volume=46 \|issue=12 \|pages=3304–3314 \|doi=10.1109/78.735305 \|bibcode=1998ITSP...46.3304H }}</ref> electric service restoration,<ref name=service_restoration>{{cite journal\|author1=Augugliaro, A. \|author2=Dusonchet, L. \|author3=Riva-Sanseverino, E. \|year=1998\|title=Service restoration in compensated distribution networks using a hybrid genetic algorithm\|journal=Electric Power Systems Research\|volume=46\|issue=1\|pages=59–66\|doi=10.1016/S0378-7796(98)00025-X\|bibcode=1998EPSR...46...59A }}</ref> medical [[expert system]]s,<ref name=medical_expert_system>{{cite journal\|author1=Wehrens, R. \|author2=Lucasius, C. \|author3=Buydens, L. \|author4=Kateman, G. \|year=1993\|title=HIPS, A hybrid self-adapting expert system for nuclear magnetic resonance spectrum interpretation using genetic algorithms\|journal=Analytica Chimica Acta\|volume=277\|issue=2\|pages=313–324\|doi=10.1016/0003-2670(93)80444-P\|bibcode=1993AcAC..277..313W \|hdl=2066/112321 \|s2cid=53954763 \|hdl-access=free}}</ref> [[single machine scheduling]],<ref name=single_machine_sched>{{cite conference\|author1=França, P. \|author2=Mendes, A. \|author3=Moscato, P. \|title=Memetic algorithms to minimize tardiness on a single machine with sequence-dependent setup times\|conference=Proceedings of the 5th International Conference of the Decision Sciences Institute\|pages=1708–1710\|year=1999\|location=Athens, Greece\|s2cid=10797987 }}</ref> automatic timetabling (notably, the timetable for the [[NHL]]),<ref name="nhl_timetabling">{{cite journal \| last=Costa \| first=Daniel \| title=An Evolutionary Tabu Search Algorithm And The NHL Scheduling Problem \| journal=INFOR: Information Systems and Operational Research \| volume=33 \| issue=3 \| year=1995 \| doi=10.1080/03155986.1995.11732279 \| pages=161–178\| s2cid=15491435 }}</ref> [[Schedule (workplace)\|manpower scheduling]],<ref name=nurse_rostering>{{cite conference\|author=Aickelin, U.\|title=Nurse rostering with genetic algorithms\|conference=Proceedings of young operational research conference 1998\|year=1998\|location=Guildford, UK\|arxiv=1004.2870}}</ref> [[nurse rostering problem\|nurse rostering optimisation]],<ref name=nurse_rostering_function_opt>{{cite book\| author = Ozcan, E.\|title=Practice and Theory of Automated Timetabling VI\|year=2007\|chapter=Memes, Self-generation and Nurse Rostering\|volume=3867\|pages=85–104\|publisher=Springer-Verlag\|doi=10.1007/978-3-540-77345-0_6\|series=Lecture Notes in Computer Science\|isbn=978-3-540-77344-3}}</ref> [[processor allocation]],<ref name=proc_alloc>{{cite journal\|author1=Ozcan, E. \|author2=Onbasioglu, E. \|year=2007\|title=Memetic Algorithms for Parallel Code Optimization\|journal=International Journal of Parallel Programming\|volume=35\|issue=1\|pages=33–61\|doi=10.1007/s10766-006-0026-x\|s2cid=15182941 }}</ref> maintenance scheduling (for example, of an electric distribution network),<ref name=planned_maintenance>{{cite journal\|author1=Burke, E. \|author2=Smith, A. \|year=1999\|title=A memetic algorithm to schedule planned maintenance for the national grid\| journal=Journal of Experimental Algorithmics \|issue=4\|pages=1–13 \|doi=10.1145/347792.347801 \|volume=4\|s2cid=17174080 \|doi-access=free}}</ref> [[Scheduling (production processes)\|scheduling]] of multiple [[Workflow\|workflows]] to constrained heterogeneous resources,<ref>{{Cite journal \|last1=Jakob \|first1=Wilfried \|last2=Strack \|first2=Sylvia \|last3=Quinte \|first3=Alexander \|last4=Bengel \|first4=Günther \|last5=Stucky \|first5=Karl-Uwe \|last6=Süß \|first6=Wolfgang \|date=2013-04-22 \|title=Fast Rescheduling of Multiple Workflows to Constrained Heterogeneous Resources Using Multi-Criteria Memetic Computing \|journal=Algorithms \|language=en \|volume=6 \|issue=2 \|pages=245–277 \|doi=10.3390/a6020245 \|issn=1999-4893\|doi-access=free }}</ref> multidimensional knapsack problem,<ref name=mkp_ma>{{cite journal\|author1=Ozcan, E. \|author2=Basaran, C. \|year=2009\|title=A Case Study of Memetic Algorithms for Constraint Optimization\|journal=Soft Computing: A Fusion of Foundations, Methodologies and Applications \|volume=13\|issue=8–9 \|pages=871–882 \|doi=10.1007/s00500-008-0354-4 \|citeseerx=10.1.1.368.7327 \|s2cid=17032624 }}</ref> [[VLSI]] design,<ref name="vlsi_design">{{cite journal \|author=Areibi \|first1=S. \|last2=Yang \|first2=Z. \|year=2004 \|title=Effective memetic algorithms for VLSI design automation = genetic algorithms + local search + multi-level clustering \|journal=Evolutionary Computation \|volume=12 \|issue=3 \|pages=327–353 \|doi=10.1162/1063656041774947 \|pmid=15355604 \|s2cid=2190268}}</ref> [[cluster analysis\|clustering]] of [[expression profiling\|gene expression profiles]],<ref name=clustering_gene_expression >{{cite book\|author1=Merz, P. \|author2=Zell, A. \|title = Parallel Problem Solving from Nature — PPSN VII\|volume=2439 \|year=2002\|publisher=[[Springer Science+Business Media\|Springer]]\|doi=10.1007/3-540-45712-7_78\|pages=811–820\| chapter=Clustering Gene Expression Profiles with Memetic Algorithms\|series=Lecture Notes in Computer Science \|isbn=978-3-540-44139-7 }}</ref> feature/gene selection,<ref name=gene_selection1>{{cite journal\|author=Zexuan Zhu, Y. S. Ong and M. Dash\|title=Markov Blanket-Embedded Genetic Algorithm for Gene Selection\|year=2007\|journal=Pattern Recognition\|volume=49\|issue=11\|pages=3236–3248\|doi=10.1016/j.patcog.2007.02.007\|bibcode=2007PatRe..40.3236Z}}</ref><ref name=gene_selection2>{{cite journal\|author=Zexuan Zhu, Y. S. Ong and M. Dash\|title=Wrapper-Filter Feature Selection Algorithm Using A Memetic Framework\|year=2007\|journal=IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics\|volume=37\|issue=1\|pages=70–76\|doi=10.1109/TSMCB.2006.883267\|pmid=17278560\|hdl=10338.dmlcz/141593\|s2cid=18382400\|hdl-access=free}}</ref> parameter determination for hardware fault injection,<ref>{{Cite web\|title=Artificial Intelligence for Fault Injection Parameter Selection {{!}} Marina Krček {{!}} Hardwear.io Webinar\|url=https://hardwear.io/webinar/AI-for-fault-injection-parameter-selection.php\|access-date=2021-05-21\|website=hardwear.io}}</ref> and multi-class, multi-objective [[feature selection]].<ref>{{Cite journal \|last1=Zhu \|first1=Zexuan \|last2=Ong \|first2=Yew-Soon \|last3=Zurada \|first3=Jacek M \|date=April 2010 \|title=Identification of Full and Partial Class Relevant Genes \|url=https://ieeexplore.ieee.org/document/4653480 \|journal=IEEE/ACM Transactions on Computational Biology and Bioinformatics \|volume=7 \|issue=2 \|pages=263–277 \|doi=10.1109/TCBB.2008.105 \|pmid=20431146 \|s2cid=2904028 \|issn=1545-5963}}</ref><ref name=feature_selection2>{{cite book\|author1=G. Karkavitsas \|author2=G. Tsihrintzis \|title=Intelligent Interactive Multimedia Systems and Services \|chapter=Automatic Music Genre Classification Using Hybrid Genetic Algorithms \|name-list-style=amp \|year=2011\|volume=11\|pages=323–335\|publisher=Springer\|doi=10.1007/978-3-642-22158-3_32\|series=Smart Innovation, Systems and Technologies \|isbn=978-3-642-22157-6 \|s2cid=15011089 }}</ref>

	==Recent activities in memetic algorithms==		==Recent activities in memetic algorithms==

GhostInTheMachine: Changing short description from "extension of the traditional genetic algorithm" to "Algorithm for searching a problem space"

2024-05-10T20:43:22Z

Changing short description from "extension of the traditional genetic algorithm" to "Algorithm for searching a problem space"

← Previous revision		Revision as of 20:43, 10 May 2024
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	{{Short description\|~~extension~~ of ~~the~~ ~~traditional~~ ~~genetic~~ ~~algorithm~~}}		{{Short description\|Algorithm for searching a problem space}}
	{{Evolutionary algorithms}}		{{Evolutionary algorithms}}
	A '''memetic algorithm''' (MA) in [[computer science]] and [[operations research]], is an extension of the traditional [[genetic algorithm]] (GA) or more general [[evolutionary algorithm]] (EA). It may provide a sufficiently good solution to an [[optimization problem]]. It uses a suitable [[heuristic]] or [[Local search (optimization)\|local search]] technique to improve the quality of solutions generated by the EA and to reduce the likelihood of [[premature convergence]].<ref>{{cite journal\|title= A Comparison between Memetic algorithm and Genetic algorithm for the cryptanalysis of Simplified Data Encryption Standard algorithm\|author=Poonam Garg \|journal=International Journal of Network Security & Its Applications (IJNSA)\|volume=1\|issue=1\|date=April 2009\|arxiv= 1004.0574 \|bibcode=2010arXiv1004.0574G }}</ref>		A '''memetic algorithm''' (MA) in [[computer science]] and [[operations research]], is an extension of the traditional [[genetic algorithm]] (GA) or more general [[evolutionary algorithm]] (EA). It may provide a sufficiently good solution to an [[optimization problem]]. It uses a suitable [[heuristic]] or [[Local search (optimization)\|local search]] technique to improve the quality of solutions generated by the EA and to reduce the likelihood of [[premature convergence]].<ref>{{cite journal\|title= A Comparison between Memetic algorithm and Genetic algorithm for the cryptanalysis of Simplified Data Encryption Standard algorithm\|author=Poonam Garg \|journal=International Journal of Network Security & Its Applications (IJNSA)\|volume=1\|issue=1\|date=April 2009\|arxiv= 1004.0574 \|bibcode=2010arXiv1004.0574G }}</ref>

Simone Biancolilla at 18:43, 10 May 2024

2024-05-10T18:43:48Z

← Previous revision		Revision as of 18:43, 10 May 2024
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			{{Short description\|extension of the traditional genetic algorithm}}
	{{Evolutionary algorithms}}		{{Evolutionary algorithms}}
	A '''memetic algorithm''' (MA) in [[computer science]] and [[operations research]], is an extension of the traditional [[genetic algorithm]] (GA) or more general [[evolutionary algorithm]] (EA). It may provide a sufficiently good solution to an [[optimization problem]]. It uses a suitable [[heuristic]] or [[Local search (optimization)\|local search]] technique to improve the quality of solutions generated by the EA and to reduce the likelihood of [[premature convergence]].<ref>{{cite journal\|title= A Comparison between Memetic algorithm and Genetic algorithm for the cryptanalysis of Simplified Data Encryption Standard algorithm\|author=Poonam Garg \|journal=International Journal of Network Security & Its Applications (IJNSA)\|volume=1\|issue=1\|date=April 2009\|arxiv= 1004.0574 \|bibcode=2010arXiv1004.0574G }}</ref>		A '''memetic algorithm''' (MA) in [[computer science]] and [[operations research]], is an extension of the traditional [[genetic algorithm]] (GA) or more general [[evolutionary algorithm]] (EA). It may provide a sufficiently good solution to an [[optimization problem]]. It uses a suitable [[heuristic]] or [[Local search (optimization)\|local search]] technique to improve the quality of solutions generated by the EA and to reduce the likelihood of [[premature convergence]].<ref>{{cite journal\|title= A Comparison between Memetic algorithm and Genetic algorithm for the cryptanalysis of Simplified Data Encryption Standard algorithm\|author=Poonam Garg \|journal=International Journal of Network Security & Its Applications (IJNSA)\|volume=1\|issue=1\|date=April 2009\|arxiv= 1004.0574 \|bibcode=2010arXiv1004.0574G }}</ref>

Jarble: linking

2024-03-22T20:29:25Z

linking

← Previous revision		Revision as of 20:29, 22 March 2024
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	In the context of complex optimization, many different instantiations of memetic algorithms have been reported across a wide range of [[#Applications\|application domains]], in general, converging to high-quality solutions more efficiently than their conventional evolutionary counterparts.<ref name=MAs-in-Data-Science-and-Business-Analytics>{{cite book\|author1=Moscato, P. \|author2=Mathieson, L. \|title = Business and Consumer Analytics: New Ideas\|year=2019\|publisher=[[Springer Science+Business Media \|Springer]] \|doi=10.1007/978-3-030-06222-4_13 \|pages=545–608 \|chapter=Memetic Algorithms for Business Analytics and Data Science: A Brief Survey\|isbn=978-3-030-06221-7\|s2cid=173187844 }}</ref>		In the context of complex optimization, many different instantiations of memetic algorithms have been reported across a wide range of [[#Applications\|application domains]], in general, converging to high-quality solutions more efficiently than their conventional evolutionary counterparts.<ref name=MAs-in-Data-Science-and-Business-Analytics>{{cite book\|author1=Moscato, P. \|author2=Mathieson, L. \|title = Business and Consumer Analytics: New Ideas\|year=2019\|publisher=[[Springer Science+Business Media \|Springer]] \|doi=10.1007/978-3-030-06222-4_13 \|pages=545–608 \|chapter=Memetic Algorithms for Business Analytics and Data Science: A Brief Survey\|isbn=978-3-030-06221-7\|s2cid=173187844 }}</ref>

	In general, using the ideas of memetics within a computational framework is called ''memetic computing'' or ''memetic computation'' (MC).<ref name=MC2011>{{cite journal\|last1=Chen\|first1=X. S. \|last2=Ong \|first2=Y. S. \|last3=Lim \|first3=M. H. \|last4=Tan \|first4=K. C. \|year=2011 \|title=A Multi-Facet Survey on Memetic Computation \|journal=[[IEEE Transactions on Evolutionary Computation]] \|volume=15 \|issue=5 \|pages=591–607 \|doi=10.1109/tevc.2011.2132725 \|s2cid=17006589 }}</ref><ref name="MC2010">{{cite journal \|last1=Chen \|first1=X. S. \|last2=Ong \|first2=Y. S. \|last3=Lim \|first3=M. H.\|year=2010 \|title=Research Frontier: Memetic Computation - Past, Present & Future \|journal=[[IEEE Computational Intelligence Society#Publications \|IEEE Computational Intelligence Magazine]] \|volume=5\|issue=2\|pages=24–36\|doi=10.1109/mci.2010.936309\|hdl=10356/148175 \|s2cid=17955514 \|hdl-access=free }}</ref> With MC, the traits of universal Darwinism are more appropriately captured. Viewed in this perspective, MA is a more constrained notion of MC. More specifically, MA covers one area of MC, in particular dealing with areas of evolutionary algorithms that marry other deterministic refinement techniques for solving optimization problems. MC extends the notion of memes to cover conceptual entities of knowledge-enhanced procedures or representations.		In general, using the ideas of memetics within a computational framework is called ''[[memetic computing]]'' or ''memetic computation'' (MC).<ref name=MC2011>{{cite journal\|last1=Chen\|first1=X. S. \|last2=Ong \|first2=Y. S. \|last3=Lim \|first3=M. H. \|last4=Tan \|first4=K. C. \|year=2011 \|title=A Multi-Facet Survey on Memetic Computation \|journal=[[IEEE Transactions on Evolutionary Computation]] \|volume=15 \|issue=5 \|pages=591–607 \|doi=10.1109/tevc.2011.2132725 \|s2cid=17006589 }}</ref><ref name="MC2010">{{cite journal \|last1=Chen \|first1=X. S. \|last2=Ong \|first2=Y. S. \|last3=Lim \|first3=M. H.\|year=2010 \|title=Research Frontier: Memetic Computation - Past, Present & Future \|journal=[[IEEE Computational Intelligence Society#Publications \|IEEE Computational Intelligence Magazine]] \|volume=5\|issue=2\|pages=24–36\|doi=10.1109/mci.2010.936309\|hdl=10356/148175 \|s2cid=17955514 \|hdl-access=free }}</ref> With MC, the traits of universal Darwinism are more appropriately captured. Viewed in this perspective, MA is a more constrained notion of MC. More specifically, MA covers one area of MC, in particular dealing with areas of evolutionary algorithms that marry other deterministic refinement techniques for solving optimization problems. MC extends the notion of memes to cover conceptual entities of knowledge-enhanced procedures or representations.

	== Theoretical Background ==		== Theoretical Background ==