Estimation of distribution algorithm - Revision history

DancingOwl: Added ACO to the "Related" list

2024-10-22T10:01:30Z

Added ACO to the "Related" list

← Previous revision		Revision as of 10:01, 22 October 2024
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	* [[CMA-ES]]		* [[CMA-ES]]
	* [[Cross-entropy method]]		* [[Cross-entropy method]]
			* [[Ant colony optimization algorithms]]

	==References==		==References==

Kku: Adding short description: "Family of stochastic optimization methods"

2024-04-27T18:47:09Z

Adding short description: "Family of stochastic optimization methods"

← Previous revision		Revision as of 18:47, 27 April 2024
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			{{Short description\|Family of stochastic optimization methods}}
	[[Image:Eda mono-variant gauss iterations.svg\|thumb\|350px\|Estimation of distribution algorithm. For each iteration ''i'', a random draw is performed for a population ''P'' in a distribution ''PDu''. The distribution parameters ''PDe'' are then estimated using the selected points ''PS''. The illustrated example optimizes a continuous objective function ''f(X)'' with a unique optimum ''O''. The sampling (following a normal distribution ''N'') concentrates around the optimum as one goes along unwinding algorithm.]]		[[Image:Eda mono-variant gauss iterations.svg\|thumb\|350px\|Estimation of distribution algorithm. For each iteration ''i'', a random draw is performed for a population ''P'' in a distribution ''PDu''. The distribution parameters ''PDe'' are then estimated using the selected points ''PS''. The illustrated example optimizes a continuous objective function ''f(X)'' with a unique optimum ''O''. The sampling (following a normal distribution ''N'') concentrates around the optimum as one goes along unwinding algorithm.]]

BD2412: clean up spacing around commas and other punctuation fixes, replaced: ,N → , N (4), ,X → , X (9), ,x → , x (3), ,y → , y, ; → ;

2024-01-06T23:45:27Z

clean up spacing around commas and other punctuation fixes, replaced: ,N → , N (4), ,X → , X (9), ,x → , x (3), ,y → , y, ; → ;

← Previous revision		Revision as of 23:45, 6 January 2024
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	[[Image:Eda mono-variant gauss iterations.svg\|thumb\|350px\|Estimation of distribution algorithm. For each iteration ''i'', a random draw is performed for a population ''P'' in a distribution ''PDu''. The distribution parameters ''PDe'' are then estimated using the selected points ''PS''. The illustrated example optimizes a continuous objective function ''f(X)'' with a unique optimum ''O''. The sampling (following a normal distribution ''N'') concentrates around the optimum as one goes along unwinding algorithm.]]		[[Image:Eda mono-variant gauss iterations.svg\|thumb\|350px\|Estimation of distribution algorithm. For each iteration ''i'', a random draw is performed for a population ''P'' in a distribution ''PDu''. The distribution parameters ''PDe'' are then estimated using the selected points ''PS''. The illustrated example optimizes a continuous objective function ''f(X)'' with a unique optimum ''O''. The sampling (following a normal distribution ''N'') concentrates around the optimum as one goes along unwinding algorithm.]]

	'''''Estimation of distribution algorithms''''' ('''EDAs'''), sometimes called '''''probabilistic model-building genetic algorithms''''' (PMBGAs),<ref>{{Citation\|last=Pelikan\|first=Martin\|date=2005-02-21\|pages=13–30\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/978-3-540-32373-0_2\|isbn=9783540237747\|title=Hierarchical Bayesian Optimization Algorithm\|volume=170\|series=Studies in Fuzziness and Soft Computing\|chapter=Probabilistic Model-Building Genetic Algorithms}}</ref> are [[stochastic optimization]] methods that guide the search for the optimum by building and sampling explicit probabilistic models of promising candidate solutions. Optimization is viewed as a series of incremental updates of a probabilistic model, starting with the model encoding an uninformative prior over admissible solutions and ending with the model that generates only the global optima.<ref>{{cite book\|author1=Pedro Larrañaga\|author2=Jose A. Lozano\|title=Estimation of Distribution Algorithms a New Tool for Evolutionary Computation\|date=2002\|publisher=Springer US\|location=Boston, MA\|isbn=978-1-4615-1539-5}}</ref><ref>{{cite book\|author1=Jose A. Lozano\|author2=Larrañaga, P.\|author3=Inza, I.\|author4=Bengoetxea, E.\|title=Towards a new evolutionary computation advances in the estimation of distribution algorithms\|date=2006\|publisher=Springer\|location=Berlin\|isbn=978-3-540-32494-2}}</ref><ref>{{cite book\|last1=Pelikan\|first1=Martin\|last2=Sastry\|first2=Kumara\|last3=Cantú-Paz\|first3=Erick\|title=Scalable optimization via probabilistic modeling : from algorithms to applications ; with 26 tables\|date=2006\|publisher=Springer\|location=Berlin\|isbn=978-3540349532}}</ref>		'''''Estimation of distribution algorithms''''' ('''EDAs'''), sometimes called '''''probabilistic model-building genetic algorithms''''' (PMBGAs),<ref>{{Citation\|last=Pelikan\|first=Martin\|date=2005-02-21\|pages=13–30\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/978-3-540-32373-0_2\|isbn=9783540237747\|title=Hierarchical Bayesian Optimization Algorithm\|volume=170\|series=Studies in Fuzziness and Soft Computing\|chapter=Probabilistic Model-Building Genetic Algorithms}}</ref> are [[stochastic optimization]] methods that guide the search for the optimum by building and sampling explicit probabilistic models of promising candidate solutions. Optimization is viewed as a series of incremental updates of a probabilistic model, starting with the model encoding an uninformative prior over admissible solutions and ending with the model that generates only the global optima.<ref>{{cite book\|author1=Pedro Larrañaga\|author2=Jose A. Lozano\|title=Estimation of Distribution Algorithms a New Tool for Evolutionary Computation\|date=2002\|publisher=Springer US\|location=Boston, MA\|isbn=978-1-4615-1539-5}}</ref><ref>{{cite book\|author1=Jose A. Lozano\|author2=Larrañaga, P.\|author3=Inza, I.\|author4=Bengoetxea, E.\|title=Towards a new evolutionary computation advances in the estimation of distribution algorithms\|date=2006\|publisher=Springer\|location=Berlin\|isbn=978-3-540-32494-2}}</ref><ref>{{cite book\|last1=Pelikan\|first1=Martin\|last2=Sastry\|first2=Kumara\|last3=Cantú-Paz\|first3=Erick\|title=Scalable optimization via probabilistic modeling : from algorithms to applications; with 26 tables\|date=2006\|publisher=Springer\|location=Berlin\|isbn=978-3540349532}}</ref>

	EDAs belong to the class of [[evolutionary algorithms]]. The main difference between EDAs and most conventional evolutionary algorithms is that evolutionary algorithms generate new candidate solutions using an ''implicit'' distribution defined by one or more variation operators, whereas EDAs use an ''explicit'' probability distribution encoded by a [[Bayesian network]], a [[multivariate normal distribution]], or another model class. Similarly as other evolutionary algorithms, EDAs can be used to solve optimization problems defined over a number of representations from vectors to [[LISP]] style S expressions, and the quality of candidate solutions is often evaluated using one or more objective functions.		EDAs belong to the class of [[evolutionary algorithms]]. The main difference between EDAs and most conventional evolutionary algorithms is that evolutionary algorithms generate new candidate solutions using an ''implicit'' distribution defined by one or more variation operators, whereas EDAs use an ''explicit'' probability distribution encoded by a [[Bayesian network]], a [[multivariate normal distribution]], or another model class. Similarly as other evolutionary algorithms, EDAs can be used to solve optimization problems defined over a number of representations from vectors to [[LISP]] style S expressions, and the quality of candidate solutions is often evaluated using one or more objective functions.

Ozzie10aaaa: Cleaned up using AutoEd

2023-12-12T17:43:56Z

Cleaned up using AutoEd

← Previous revision		Revision as of 17:43, 12 December 2023
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	<math>x_i[\tau]</math>:= <math>x_j[\tau]</math>		<math>x_i[\tau]</math>:= <math>x_j[\tau]</math>
	'''if''' <math>f(x_i) \leq f_{x_i}</math> '''then'''		'''if''' <math>f(x_i) \leq f_{x_i}</math> '''then'''
	<math>x_i[\tau]:= x_j[\tau]</math>		<math>x_i[\tau]:= x_j[\tau]</math>
	'''return''' <math>P(t)</math>		'''return''' <math>P(t)</math>
	{{algorithm-end}}		{{algorithm-end}}
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	* Estimation of Gaussian networks algorithm (EGNA){{Citation needed\|date=June 2018}}		* Estimation of Gaussian networks algorithm (EGNA){{Citation needed\|date=June 2018}}
	* Estimation multivariate normal algorithm with thresheld convergence<ref>{{Cite book\|last1=Tamayo-Vera\|first1=Dania\|last2=Bolufe-Rohler\|first2=Antonio\|last3=Chen\|first3=Stephen\|title=2016 IEEE Congress on Evolutionary Computation (CEC) \|chapter=Estimation multivariate normal algorithm with thresheld convergence \|date=2016\|pages=3425–3432 \|language=en-US\|publisher=IEEE\|doi=10.1109/cec.2016.7744223\|isbn=9781509006236\|s2cid=33114730 }}</ref>		* Estimation multivariate normal algorithm with thresheld convergence<ref>{{Cite book\|last1=Tamayo-Vera\|first1=Dania\|last2=Bolufe-Rohler\|first2=Antonio\|last3=Chen\|first3=Stephen\|title=2016 IEEE Congress on Evolutionary Computation (CEC) \|chapter=Estimation multivariate normal algorithm with thresheld convergence \|date=2016\|pages=3425–3432 \|language=en-US\|publisher=IEEE\|doi=10.1109/cec.2016.7744223\|isbn=9781509006236\|s2cid=33114730 }}</ref>
	*Dependency Structure Matrix Genetic Algorithm (DSMGA)<ref>{{Citation\|last1=Yu\|first1=Tian-Li\|title=Genetic Algorithm Design Inspired by Organizational Theory: Pilot Study of a Dependency Structure Matrix Driven Genetic Algorithm\|date=2003\|work=Genetic and Evolutionary Computation — GECCO 2003\|pages=1620–1621\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/3-540-45110-2_54\|isbn=9783540406037\|last2=Goldberg\|first2=David E.\|last3=Yassine\|first3=Ali\|last4=Chen\|first4=Ying-Ping}}</ref><ref>{{Cite book\|last1=Hsu\|first1=Shih-Huan\|last2=Yu\|first2=Tian-Li\|date=2015-07-11\|title=Optimization by Pairwise Linkage Detection, Incremental Linkage Set, and Restricted / Back Mixing: DSMGA-II\|publisher=ACM\|pages=519–526\|doi=10.1145/2739480.2754737\|isbn=9781450334723\|arxiv=1807.11669\|s2cid=17031156 }}</ref>		* Dependency Structure Matrix Genetic Algorithm (DSMGA)<ref>{{Citation\|last1=Yu\|first1=Tian-Li\|title=Genetic Algorithm Design Inspired by Organizational Theory: Pilot Study of a Dependency Structure Matrix Driven Genetic Algorithm\|date=2003\|work=Genetic and Evolutionary Computation — GECCO 2003\|pages=1620–1621\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/3-540-45110-2_54\|isbn=9783540406037\|last2=Goldberg\|first2=David E.\|last3=Yassine\|first3=Ali\|last4=Chen\|first4=Ying-Ping}}</ref><ref>{{Cite book\|last1=Hsu\|first1=Shih-Huan\|last2=Yu\|first2=Tian-Li\|date=2015-07-11\|title=Optimization by Pairwise Linkage Detection, Incremental Linkage Set, and Restricted / Back Mixing: DSMGA-II\|publisher=ACM\|pages=519–526\|doi=10.1145/2739480.2754737\|isbn=9781450334723\|arxiv=1807.11669\|s2cid=17031156 }}</ref>

	==Related==		==Related==

	* [[CMA-ES]]		* [[CMA-ES]]
	* [[Cross-entropy method]]		* [[Cross-entropy method]]

Citation bot: Alter: title. Add: title, chapter. Removed parameters. Some additions/deletions were parameter name changes. | Use this bot. Report bugs. | #UCB_CommandLine

2023-08-24T13:21:50Z

Alter: title. Add: title, chapter. Removed parameters. Some additions/deletions were parameter name changes. | Use this bot. Report bugs. | #UCB_CommandLine

← Previous revision		Revision as of 13:21, 24 August 2023
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	===Bivariate marginal distribution algorithm (BMDA)===		===Bivariate marginal distribution algorithm (BMDA)===
	The BMDA<ref>{{cite book\|last1=Pelikan\|first1=Martin\|last2=Muehlenbein\|first2=Heinz\|title=The Bivariate Marginal Distribution Algorithm~~\|journal=Advances~~ ~~in Soft Computing~~\|date=1 January 1999\|pages=521–535\|doi=10.1007/978-1-4471-0819-1_39\|isbn=978-1-85233-062-0\|citeseerx=10.1.1.55.1151}}</ref> factorizes the joint probability distribution in bivariate distributions. First, a randomly chosen variable is added as a node in a graph, the most dependent variable to one of those in the graph is chosen among those not yet in the graph, this procedure is repeated until no remaining variable depends on any variable in the graph (verified according to a threshold value).		The BMDA<ref>{{cite book\|last1=Pelikan\|first1=Martin\|last2=Muehlenbein\|first2=Heinz\|title=Advances in Soft Computing \|chapter=The Bivariate Marginal Distribution Algorithm \|date=1 January 1999\|pages=521–535\|doi=10.1007/978-1-4471-0819-1_39\|isbn=978-1-85233-062-0\|citeseerx=10.1.1.55.1151}}</ref> factorizes the joint probability distribution in bivariate distributions. First, a randomly chosen variable is added as a node in a graph, the most dependent variable to one of those in the graph is chosen among those not yet in the graph, this procedure is repeated until no remaining variable depends on any variable in the graph (verified according to a threshold value).

	The resulting model is a forest with multiple trees rooted at nodes <math>\Upsilon_t</math>. Considering <math>I_t</math> the non-root variables, BMDA estimates a factorized distribution in which the root variables can be sampled independently, whereas all the others must be conditioned to the parent variable <math>\pi_i</math>.		The resulting model is a forest with multiple trees rooted at nodes <math>\Upsilon_t</math>. Considering <math>I_t</math> the non-root variables, BMDA estimates a factorized distribution in which the root variables can be sampled independently, whereas all the others must be conditioned to the parent variable <math>\pi_i</math>.
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	===Linkage-tree Genetic Algorithm (LTGA)===		===Linkage-tree Genetic Algorithm (LTGA)===
	The LTGA<ref>{{cite book\|last1=Thierens\|first1=Dirk\|~~chapter=The Linkage Tree Genetic Algorithm\|journal~~=Parallel Problem Solving from Nature, PPSN XI\|date=11 September 2010\|pages=264–273\|doi=10.1007/978-3-642-15844-5_27\|isbn=978-3-642-15843-8}}</ref> differs from most EDA in the sense it does not explicitly model a probability distribution but only a linkage model, called linkage-tree. A linkage <math>T</math> is a set of linkage sets with no probability distribution associated, therefore, there is no way to sample new solutions directly from <math>T</math>. The linkage model is a linkage-tree produced stored as a [[Family of sets]] (FOS).		The LTGA<ref>{{cite book\|last1=Thierens\|first1=Dirk\|title=Parallel Problem Solving from Nature, PPSN XI \|chapter=The Linkage Tree Genetic Algorithm\|date=11 September 2010\|pages=264–273\|doi=10.1007/978-3-642-15844-5_27\|isbn=978-3-642-15843-8}}</ref> differs from most EDA in the sense it does not explicitly model a probability distribution but only a linkage model, called linkage-tree. A linkage <math>T</math> is a set of linkage sets with no probability distribution associated, therefore, there is no way to sample new solutions directly from <math>T</math>. The linkage model is a linkage-tree produced stored as a [[Family of sets]] (FOS).

	<math>		<math>

Citation bot: Alter: title. Add: pages, chapter. Removed parameters. | Use this bot. Report bugs. | #UCB_CommandLine

2023-07-22T01:15:02Z

Alter: title. Add: pages, chapter. Removed parameters. | Use this bot. Report bugs. | #UCB_CommandLine

← Previous revision		Revision as of 01:15, 22 July 2023
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	* Real-coded PBIL{{Citation needed\|date=June 2018}}		* Real-coded PBIL{{Citation needed\|date=June 2018}}
	* Selfish Gene Algorithm (SG)<ref>{{Cite book\|last1=Corno\|first1=Fulvio\|last2=Reorda\|first2=Matteo Sonza\|last3=Squillero\|first3=Giovanni\|date=1998-02-27\|title=The selfish gene algorithm: a new evolutionary optimization strategy\|publisher=ACM\|pages=349–355\|doi=10.1145/330560.330838\|isbn=978-0897919692\|s2cid=9125252 }}</ref>		* Selfish Gene Algorithm (SG)<ref>{{Cite book\|last1=Corno\|first1=Fulvio\|last2=Reorda\|first2=Matteo Sonza\|last3=Squillero\|first3=Giovanni\|date=1998-02-27\|title=The selfish gene algorithm: a new evolutionary optimization strategy\|publisher=ACM\|pages=349–355\|doi=10.1145/330560.330838\|isbn=978-0897919692\|s2cid=9125252 }}</ref>
	* Compact Differential Evolution (cDE)<ref>{{Cite journal\|last1=Mininno\|first1=Ernesto\|last2=Neri\|first2=Ferrante\|last3=Cupertino\|first3=Francesco\|last4=Naso\|first4=David\|date=2011\|title=Compact Differential Evolution\|journal=IEEE Transactions on Evolutionary Computation\|language=en-US\|volume=15\|issue=1\|pages=32–54\|doi=10.1109/tevc.2010.2058120\|s2cid=20582233 \|issn=1089-778X}}</ref> and its variants<ref>{{Cite journal\|last1=Iacca\|first1=Giovanni\|last2=Caraffini\|first2=Fabio\|last3=Neri\|first3=Ferrante\|date=2012\|title=Compact Differential Evolution Light: High Performance Despite Limited Memory Requirement and Modest Computational Overhead\|journal=Journal of Computer Science and Technology\|language=en\|volume=27\|issue=5\|pages=1056–1076\|doi=10.1007/s11390-012-1284-2\|s2cid=3184035 \|issn=1000-9000}}</ref><ref>{{Citation\|last1=Iacca\|first1=Giovanni\|title=Opposition-Based Learning in Compact Differential Evolution\|date=2011\|last2=Neri\|first2=Ferrante\|last3=Mininno\|first3=Ernesto\|work=Applications of Evolutionary Computation\|pages=264–273\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/978-3-642-20525-5_27\|isbn=9783642205248}}</ref><ref>{{Cite book\|last1=Mallipeddi\|first1=Rammohan\|last2=Iacca\|first2=Giovanni\|last3=Suganthan\|first3=Ponnuthurai Nagaratnam\|last4=Neri\|first4=Ferrante\|last5=Mininno\|first5=Ernesto\|~~date~~=2011\|~~title~~=Ensemble strategies in Compact Differential Evolution\|~~journal~~=2011 ~~IEEE Congress of Evolutionary Computation (CEC)~~\|language=en-US\|publisher=IEEE\|doi=10.1109/cec.2011.5949857\|isbn=9781424478347\|s2cid=11781300 }}</ref><ref>{{Cite journal\|last1=Neri\|first1=Ferrante\|last2=Iacca\|first2=Giovanni\|last3=Mininno\|first3=Ernesto\|date=2011\|title=Disturbed Exploitation compact Differential Evolution for limited memory optimization problems\|journal=Information Sciences\|volume=181\|issue=12\|pages=2469–2487\|doi=10.1016/j.ins.2011.02.004\|issn=0020-0255}}</ref><ref>{{Cite book\|last1=Iacca\|first1=Giovanni\|last2=Mallipeddi\|first2=Rammohan\|last3=Mininno\|first3=Ernesto\|last4=Neri\|first4=Ferrante\|last5=Suganthan\|first5=Pannuthurai Nagaratnam~~\|date=2011~~\|title=~~Global~~ ~~supervision~~ ~~for~~ ~~compact~~ Differential Evolution\|~~journal~~=~~2011~~ ~~IEEE~~ ~~Symposium~~ on Differential Evolution ~~(SDE)~~\|language=en-US\|publisher=IEEE\|doi=10.1109/sde.2011.5952051\|isbn=9781612840710\|s2cid=8874851 }}</ref><ref>{{Cite book\|last1=Iacca\|first1=Giovanni\|last2=Mallipeddi\|first2=Rammohan\|last3=Mininno\|first3=Ernesto\|last4=Neri\|first4=Ferrante\|last5=Suganthan\|first5=Pannuthurai Nagaratnam\|~~date~~=2011\|~~title~~=Super-fit and population size reduction in compact Differential Evolution\|~~journal~~=2011 ~~IEEE Workshop on Memetic Computing (MC)~~\|language=en-US\|publisher=IEEE\|doi=10.1109/mc.2011.5953633\|isbn=9781612840659\|s2cid=5692951 }}</ref>		* Compact Differential Evolution (cDE)<ref>{{Cite journal\|last1=Mininno\|first1=Ernesto\|last2=Neri\|first2=Ferrante\|last3=Cupertino\|first3=Francesco\|last4=Naso\|first4=David\|date=2011\|title=Compact Differential Evolution\|journal=IEEE Transactions on Evolutionary Computation\|language=en-US\|volume=15\|issue=1\|pages=32–54\|doi=10.1109/tevc.2010.2058120\|s2cid=20582233 \|issn=1089-778X}}</ref> and its variants<ref>{{Cite journal\|last1=Iacca\|first1=Giovanni\|last2=Caraffini\|first2=Fabio\|last3=Neri\|first3=Ferrante\|date=2012\|title=Compact Differential Evolution Light: High Performance Despite Limited Memory Requirement and Modest Computational Overhead\|journal=Journal of Computer Science and Technology\|language=en\|volume=27\|issue=5\|pages=1056–1076\|doi=10.1007/s11390-012-1284-2\|s2cid=3184035 \|issn=1000-9000}}</ref><ref>{{Citation\|last1=Iacca\|first1=Giovanni\|title=Opposition-Based Learning in Compact Differential Evolution\|date=2011\|last2=Neri\|first2=Ferrante\|last3=Mininno\|first3=Ernesto\|work=Applications of Evolutionary Computation\|pages=264–273\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/978-3-642-20525-5_27\|isbn=9783642205248}}</ref><ref>{{Cite book\|last1=Mallipeddi\|first1=Rammohan\|last2=Iacca\|first2=Giovanni\|last3=Suganthan\|first3=Ponnuthurai Nagaratnam\|last4=Neri\|first4=Ferrante\|last5=Mininno\|first5=Ernesto\|title=2011 IEEE Congress of Evolutionary Computation (CEC) \|chapter=Ensemble strategies in Compact Differential Evolution \|date=2011\|pages=1972–1977 \|language=en-US\|publisher=IEEE\|doi=10.1109/cec.2011.5949857\|isbn=9781424478347\|s2cid=11781300 }}</ref><ref>{{Cite journal\|last1=Neri\|first1=Ferrante\|last2=Iacca\|first2=Giovanni\|last3=Mininno\|first3=Ernesto\|date=2011\|title=Disturbed Exploitation compact Differential Evolution for limited memory optimization problems\|journal=Information Sciences\|volume=181\|issue=12\|pages=2469–2487\|doi=10.1016/j.ins.2011.02.004\|issn=0020-0255}}</ref><ref>{{Cite book\|last1=Iacca\|first1=Giovanni\|last2=Mallipeddi\|first2=Rammohan\|last3=Mininno\|first3=Ernesto\|last4=Neri\|first4=Ferrante\|last5=Suganthan\|first5=Pannuthurai Nagaratnam\|title=2011 IEEE Symposium on Differential Evolution (SDE) \|chapter=Global supervision for compact Differential Evolution \|date=2011\|pages=1–8 \|language=en-US\|publisher=IEEE\|doi=10.1109/sde.2011.5952051\|isbn=9781612840710\|s2cid=8874851 }}</ref><ref>{{Cite book\|last1=Iacca\|first1=Giovanni\|last2=Mallipeddi\|first2=Rammohan\|last3=Mininno\|first3=Ernesto\|last4=Neri\|first4=Ferrante\|last5=Suganthan\|first5=Pannuthurai Nagaratnam\|title=2011 IEEE Workshop on Memetic Computing (MC) \|chapter=Super-fit and population size reduction in compact Differential Evolution \|date=2011\|pages=1–8 \|language=en-US\|publisher=IEEE\|doi=10.1109/mc.2011.5953633\|isbn=9781612840659\|s2cid=5692951 }}</ref>
	* Compact Particle Swarm Optimization (cPSO)<ref>{{Cite journal\|last1=Neri\|first1=Ferrante\|last2=Mininno\|first2=Ernesto\|last3=Iacca\|first3=Giovanni\|date=2013\|title=Compact Particle Swarm Optimization\|journal=Information Sciences\|volume=239\|pages=96–121\|doi=10.1016/j.ins.2013.03.026\|issn=0020-0255}}</ref>		* Compact Particle Swarm Optimization (cPSO)<ref>{{Cite journal\|last1=Neri\|first1=Ferrante\|last2=Mininno\|first2=Ernesto\|last3=Iacca\|first3=Giovanni\|date=2013\|title=Compact Particle Swarm Optimization\|journal=Information Sciences\|volume=239\|pages=96–121\|doi=10.1016/j.ins.2013.03.026\|issn=0020-0255}}</ref>
	* Compact Bacterial Foraging Optimization (cBFO)<ref>{{Citation\|last1=Iacca\|first1=Giovanni\|title=Compact Bacterial Foraging Optimization\|date=2012\|last2=Neri\|first2=Ferrante\|last3=Mininno\|first3=Ernesto\|work=Swarm and Evolutionary Computation\|pages=84–92\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/978-3-642-29353-5_10\|hdl=11572/196442 \|isbn=9783642293528}}</ref>		* Compact Bacterial Foraging Optimization (cBFO)<ref>{{Citation\|last1=Iacca\|first1=Giovanni\|title=Compact Bacterial Foraging Optimization\|date=2012\|last2=Neri\|first2=Ferrante\|last3=Mininno\|first3=Ernesto\|work=Swarm and Evolutionary Computation\|pages=84–92\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/978-3-642-29353-5_10\|hdl=11572/196442 \|isbn=9783642293528}}</ref>
	* Probabilistic incremental program evolution (PIPE)<ref>{{Cite journal\|last1=Salustowicz\|first1=null\|last2=Schmidhuber\|first2=null\|date=1997\|title=Probabilistic incremental program evolution\|journal=Evolutionary Computation\|volume=5\|issue=2\|pages=123–141\|issn=1530-9304\|pmid=10021756\|doi=10.1162/evco.1997.5.2.123\|s2cid=10759266 \|url=http://depositonce.tu-berlin.de/handle/11303/1046}}</ref>		* Probabilistic incremental program evolution (PIPE)<ref>{{Cite journal\|last1=Salustowicz\|first1=null\|last2=Schmidhuber\|first2=null\|date=1997\|title=Probabilistic incremental program evolution\|journal=Evolutionary Computation\|volume=5\|issue=2\|pages=123–141\|issn=1530-9304\|pmid=10021756\|doi=10.1162/evco.1997.5.2.123\|s2cid=10759266 \|url=http://depositonce.tu-berlin.de/handle/11303/1046}}</ref>
	* Estimation of Gaussian networks algorithm (EGNA){{Citation needed\|date=June 2018}}		* Estimation of Gaussian networks algorithm (EGNA){{Citation needed\|date=June 2018}}
	* Estimation multivariate normal algorithm with thresheld convergence<ref>{{Cite book\|last1=Tamayo-Vera\|first1=Dania\|last2=Bolufe-Rohler\|first2=Antonio\|last3=Chen\|first3=Stephen\|~~date~~=2016\|~~title~~=Estimation multivariate normal algorithm with thresheld convergence\|~~journal~~=2016 ~~IEEE Congress on Evolutionary Computation (CEC)~~\|language=en-US\|publisher=IEEE\|doi=10.1109/cec.2016.7744223\|isbn=9781509006236\|s2cid=33114730 }}</ref>		* Estimation multivariate normal algorithm with thresheld convergence<ref>{{Cite book\|last1=Tamayo-Vera\|first1=Dania\|last2=Bolufe-Rohler\|first2=Antonio\|last3=Chen\|first3=Stephen\|title=2016 IEEE Congress on Evolutionary Computation (CEC) \|chapter=Estimation multivariate normal algorithm with thresheld convergence \|date=2016\|pages=3425–3432 \|language=en-US\|publisher=IEEE\|doi=10.1109/cec.2016.7744223\|isbn=9781509006236\|s2cid=33114730 }}</ref>
	*Dependency Structure Matrix Genetic Algorithm (DSMGA)<ref>{{Citation\|last1=Yu\|first1=Tian-Li\|title=Genetic Algorithm Design Inspired by Organizational Theory: Pilot Study of a Dependency Structure Matrix Driven Genetic Algorithm\|date=2003\|work=Genetic and Evolutionary Computation — GECCO 2003\|pages=1620–1621\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/3-540-45110-2_54\|isbn=9783540406037\|last2=Goldberg\|first2=David E.\|last3=Yassine\|first3=Ali\|last4=Chen\|first4=Ying-Ping}}</ref><ref>{{Cite book\|last1=Hsu\|first1=Shih-Huan\|last2=Yu\|first2=Tian-Li\|date=2015-07-11\|title=Optimization by Pairwise Linkage Detection, Incremental Linkage Set, and Restricted / Back Mixing: DSMGA-II\|publisher=ACM\|pages=519–526\|doi=10.1145/2739480.2754737\|isbn=9781450334723\|arxiv=1807.11669\|s2cid=17031156 }}</ref>		*Dependency Structure Matrix Genetic Algorithm (DSMGA)<ref>{{Citation\|last1=Yu\|first1=Tian-Li\|title=Genetic Algorithm Design Inspired by Organizational Theory: Pilot Study of a Dependency Structure Matrix Driven Genetic Algorithm\|date=2003\|work=Genetic and Evolutionary Computation — GECCO 2003\|pages=1620–1621\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/3-540-45110-2_54\|isbn=9783540406037\|last2=Goldberg\|first2=David E.\|last3=Yassine\|first3=Ali\|last4=Chen\|first4=Ying-Ping}}</ref><ref>{{Cite book\|last1=Hsu\|first1=Shih-Huan\|last2=Yu\|first2=Tian-Li\|date=2015-07-11\|title=Optimization by Pairwise Linkage Detection, Incremental Linkage Set, and Restricted / Back Mixing: DSMGA-II\|publisher=ACM\|pages=519–526\|doi=10.1145/2739480.2754737\|isbn=9781450334723\|arxiv=1807.11669\|s2cid=17031156 }}</ref>

Citation bot: Add: hdl. | Use this bot. Report bugs. | Suggested by Corvus florensis | #UCB_webform 3412/3500

2023-02-11T10:52:20Z

Add: hdl. | Use this bot. Report bugs. | Suggested by Corvus florensis | #UCB_webform 3412/3500

← Previous revision		Revision as of 10:52, 11 February 2023
Line 194:		Line 194:
	* Compact Differential Evolution (cDE)<ref>{{Cite journal\|last1=Mininno\|first1=Ernesto\|last2=Neri\|first2=Ferrante\|last3=Cupertino\|first3=Francesco\|last4=Naso\|first4=David\|date=2011\|title=Compact Differential Evolution\|journal=IEEE Transactions on Evolutionary Computation\|language=en-US\|volume=15\|issue=1\|pages=32–54\|doi=10.1109/tevc.2010.2058120\|s2cid=20582233 \|issn=1089-778X}}</ref> and its variants<ref>{{Cite journal\|last1=Iacca\|first1=Giovanni\|last2=Caraffini\|first2=Fabio\|last3=Neri\|first3=Ferrante\|date=2012\|title=Compact Differential Evolution Light: High Performance Despite Limited Memory Requirement and Modest Computational Overhead\|journal=Journal of Computer Science and Technology\|language=en\|volume=27\|issue=5\|pages=1056–1076\|doi=10.1007/s11390-012-1284-2\|s2cid=3184035 \|issn=1000-9000}}</ref><ref>{{Citation\|last1=Iacca\|first1=Giovanni\|title=Opposition-Based Learning in Compact Differential Evolution\|date=2011\|last2=Neri\|first2=Ferrante\|last3=Mininno\|first3=Ernesto\|work=Applications of Evolutionary Computation\|pages=264–273\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/978-3-642-20525-5_27\|isbn=9783642205248}}</ref><ref>{{Cite book\|last1=Mallipeddi\|first1=Rammohan\|last2=Iacca\|first2=Giovanni\|last3=Suganthan\|first3=Ponnuthurai Nagaratnam\|last4=Neri\|first4=Ferrante\|last5=Mininno\|first5=Ernesto\|date=2011\|title=Ensemble strategies in Compact Differential Evolution\|journal=2011 IEEE Congress of Evolutionary Computation (CEC)\|language=en-US\|publisher=IEEE\|doi=10.1109/cec.2011.5949857\|isbn=9781424478347\|s2cid=11781300 }}</ref><ref>{{Cite journal\|last1=Neri\|first1=Ferrante\|last2=Iacca\|first2=Giovanni\|last3=Mininno\|first3=Ernesto\|date=2011\|title=Disturbed Exploitation compact Differential Evolution for limited memory optimization problems\|journal=Information Sciences\|volume=181\|issue=12\|pages=2469–2487\|doi=10.1016/j.ins.2011.02.004\|issn=0020-0255}}</ref><ref>{{Cite book\|last1=Iacca\|first1=Giovanni\|last2=Mallipeddi\|first2=Rammohan\|last3=Mininno\|first3=Ernesto\|last4=Neri\|first4=Ferrante\|last5=Suganthan\|first5=Pannuthurai Nagaratnam\|date=2011\|title=Global supervision for compact Differential Evolution\|journal=2011 IEEE Symposium on Differential Evolution (SDE)\|language=en-US\|publisher=IEEE\|doi=10.1109/sde.2011.5952051\|isbn=9781612840710\|s2cid=8874851 }}</ref><ref>{{Cite book\|last1=Iacca\|first1=Giovanni\|last2=Mallipeddi\|first2=Rammohan\|last3=Mininno\|first3=Ernesto\|last4=Neri\|first4=Ferrante\|last5=Suganthan\|first5=Pannuthurai Nagaratnam\|date=2011\|title=Super-fit and population size reduction in compact Differential Evolution\|journal=2011 IEEE Workshop on Memetic Computing (MC)\|language=en-US\|publisher=IEEE\|doi=10.1109/mc.2011.5953633\|isbn=9781612840659\|s2cid=5692951 }}</ref>		* Compact Differential Evolution (cDE)<ref>{{Cite journal\|last1=Mininno\|first1=Ernesto\|last2=Neri\|first2=Ferrante\|last3=Cupertino\|first3=Francesco\|last4=Naso\|first4=David\|date=2011\|title=Compact Differential Evolution\|journal=IEEE Transactions on Evolutionary Computation\|language=en-US\|volume=15\|issue=1\|pages=32–54\|doi=10.1109/tevc.2010.2058120\|s2cid=20582233 \|issn=1089-778X}}</ref> and its variants<ref>{{Cite journal\|last1=Iacca\|first1=Giovanni\|last2=Caraffini\|first2=Fabio\|last3=Neri\|first3=Ferrante\|date=2012\|title=Compact Differential Evolution Light: High Performance Despite Limited Memory Requirement and Modest Computational Overhead\|journal=Journal of Computer Science and Technology\|language=en\|volume=27\|issue=5\|pages=1056–1076\|doi=10.1007/s11390-012-1284-2\|s2cid=3184035 \|issn=1000-9000}}</ref><ref>{{Citation\|last1=Iacca\|first1=Giovanni\|title=Opposition-Based Learning in Compact Differential Evolution\|date=2011\|last2=Neri\|first2=Ferrante\|last3=Mininno\|first3=Ernesto\|work=Applications of Evolutionary Computation\|pages=264–273\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/978-3-642-20525-5_27\|isbn=9783642205248}}</ref><ref>{{Cite book\|last1=Mallipeddi\|first1=Rammohan\|last2=Iacca\|first2=Giovanni\|last3=Suganthan\|first3=Ponnuthurai Nagaratnam\|last4=Neri\|first4=Ferrante\|last5=Mininno\|first5=Ernesto\|date=2011\|title=Ensemble strategies in Compact Differential Evolution\|journal=2011 IEEE Congress of Evolutionary Computation (CEC)\|language=en-US\|publisher=IEEE\|doi=10.1109/cec.2011.5949857\|isbn=9781424478347\|s2cid=11781300 }}</ref><ref>{{Cite journal\|last1=Neri\|first1=Ferrante\|last2=Iacca\|first2=Giovanni\|last3=Mininno\|first3=Ernesto\|date=2011\|title=Disturbed Exploitation compact Differential Evolution for limited memory optimization problems\|journal=Information Sciences\|volume=181\|issue=12\|pages=2469–2487\|doi=10.1016/j.ins.2011.02.004\|issn=0020-0255}}</ref><ref>{{Cite book\|last1=Iacca\|first1=Giovanni\|last2=Mallipeddi\|first2=Rammohan\|last3=Mininno\|first3=Ernesto\|last4=Neri\|first4=Ferrante\|last5=Suganthan\|first5=Pannuthurai Nagaratnam\|date=2011\|title=Global supervision for compact Differential Evolution\|journal=2011 IEEE Symposium on Differential Evolution (SDE)\|language=en-US\|publisher=IEEE\|doi=10.1109/sde.2011.5952051\|isbn=9781612840710\|s2cid=8874851 }}</ref><ref>{{Cite book\|last1=Iacca\|first1=Giovanni\|last2=Mallipeddi\|first2=Rammohan\|last3=Mininno\|first3=Ernesto\|last4=Neri\|first4=Ferrante\|last5=Suganthan\|first5=Pannuthurai Nagaratnam\|date=2011\|title=Super-fit and population size reduction in compact Differential Evolution\|journal=2011 IEEE Workshop on Memetic Computing (MC)\|language=en-US\|publisher=IEEE\|doi=10.1109/mc.2011.5953633\|isbn=9781612840659\|s2cid=5692951 }}</ref>
	* Compact Particle Swarm Optimization (cPSO)<ref>{{Cite journal\|last1=Neri\|first1=Ferrante\|last2=Mininno\|first2=Ernesto\|last3=Iacca\|first3=Giovanni\|date=2013\|title=Compact Particle Swarm Optimization\|journal=Information Sciences\|volume=239\|pages=96–121\|doi=10.1016/j.ins.2013.03.026\|issn=0020-0255}}</ref>		* Compact Particle Swarm Optimization (cPSO)<ref>{{Cite journal\|last1=Neri\|first1=Ferrante\|last2=Mininno\|first2=Ernesto\|last3=Iacca\|first3=Giovanni\|date=2013\|title=Compact Particle Swarm Optimization\|journal=Information Sciences\|volume=239\|pages=96–121\|doi=10.1016/j.ins.2013.03.026\|issn=0020-0255}}</ref>
	* Compact Bacterial Foraging Optimization (cBFO)<ref>{{Citation\|last1=Iacca\|first1=Giovanni\|title=Compact Bacterial Foraging Optimization\|date=2012\|last2=Neri\|first2=Ferrante\|last3=Mininno\|first3=Ernesto\|work=Swarm and Evolutionary Computation\|pages=84–92\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/978-3-642-29353-5_10\|isbn=9783642293528}}</ref>		* Compact Bacterial Foraging Optimization (cBFO)<ref>{{Citation\|last1=Iacca\|first1=Giovanni\|title=Compact Bacterial Foraging Optimization\|date=2012\|last2=Neri\|first2=Ferrante\|last3=Mininno\|first3=Ernesto\|work=Swarm and Evolutionary Computation\|pages=84–92\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/978-3-642-29353-5_10\|hdl=11572/196442 \|isbn=9783642293528}}</ref>
	* Probabilistic incremental program evolution (PIPE)<ref>{{Cite journal\|last1=Salustowicz\|first1=null\|last2=Schmidhuber\|first2=null\|date=1997\|title=Probabilistic incremental program evolution\|journal=Evolutionary Computation\|volume=5\|issue=2\|pages=123–141\|issn=1530-9304\|pmid=10021756\|doi=10.1162/evco.1997.5.2.123\|s2cid=10759266 \|url=http://depositonce.tu-berlin.de/handle/11303/1046}}</ref>		* Probabilistic incremental program evolution (PIPE)<ref>{{Cite journal\|last1=Salustowicz\|first1=null\|last2=Schmidhuber\|first2=null\|date=1997\|title=Probabilistic incremental program evolution\|journal=Evolutionary Computation\|volume=5\|issue=2\|pages=123–141\|issn=1530-9304\|pmid=10021756\|doi=10.1162/evco.1997.5.2.123\|s2cid=10759266 \|url=http://depositonce.tu-berlin.de/handle/11303/1046}}</ref>
	* Estimation of Gaussian networks algorithm (EGNA){{Citation needed\|date=June 2018}}		* Estimation of Gaussian networks algorithm (EGNA){{Citation needed\|date=June 2018}}

Citation bot: Alter: template type. Add: pages, chapter, type, s2cid, authors 1-1. Removed parameters. Some additions/deletions were parameter name changes. | Use this bot. Report bugs. | Suggested by Abductive | #UCB_webform 1637/3850

2022-07-28T01:17:57Z

Alter: template type. Add: pages, chapter, type, s2cid, authors 1-1. Removed parameters. Some additions/deletions were parameter name changes. | Use this bot. Report bugs. | Suggested by Abductive | #UCB_webform 1637/3850

Show changes

David Eppstein: /* Bivariate factorizations */ lc

2021-10-04T07:56:29Z

Bivariate factorizations: lc

← Previous revision		Revision as of 07:56, 4 October 2021
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	</math>		</math>

	Bivariate and multivariate distributions are usually represented as ~~Probabilistic~~ [[~~Graphical~~ ~~Models~~]] (graphs), in which edges denote statistical dependencies (or conditional probabilities) and vertices denote variables. To learn the structure of a PGM from data linkage-learning is employed.		Bivariate and multivariate distributions are usually represented as probabilistic [[graphical model]]s (graphs), in which edges denote statistical dependencies (or conditional probabilities) and vertices denote variables. To learn the structure of a PGM from data linkage-learning is employed.

	===Mutual information maximizing input clustering (MIMIC)===		===Mutual information maximizing input clustering (MIMIC)===

Monkbot: Task 18 (cosmetic): eval 33 templates: del empty params (8×);

2020-12-17T22:38:01Z

Task 18 (cosmetic): eval 33 templates: del empty params (8×);

← Previous revision		Revision as of 22:38, 17 December 2020
Line 192:		Line 192:
	* Real-coded PBIL{{Citation needed\|date=June 2018}}		* Real-coded PBIL{{Citation needed\|date=June 2018}}
	* Selfish Gene Algorithm (SG)<ref>{{Cite book\|last=Corno\|first=Fulvio\|last2=Reorda\|first2=Matteo Sonza\|last3=Squillero\|first3=Giovanni\|date=1998-02-27\|title=The selfish gene algorithm: a new evolutionary optimization strategy\|publisher=ACM\|pages=349–355\|doi=10.1145/330560.330838\|isbn=978-0897919692}}</ref>		* Selfish Gene Algorithm (SG)<ref>{{Cite book\|last=Corno\|first=Fulvio\|last2=Reorda\|first2=Matteo Sonza\|last3=Squillero\|first3=Giovanni\|date=1998-02-27\|title=The selfish gene algorithm: a new evolutionary optimization strategy\|publisher=ACM\|pages=349–355\|doi=10.1145/330560.330838\|isbn=978-0897919692}}</ref>
	* Compact Differential Evolution (cDE)<ref>{{Cite journal\|last=Mininno\|first=Ernesto\|last2=Neri\|first2=Ferrante\|last3=Cupertino\|first3=Francesco\|last4=Naso\|first4=David\|date=2011\|title=Compact Differential Evolution\|journal=IEEE Transactions on Evolutionary Computation\|language=en-US\|volume=15\|issue=1\|pages=32–54\|doi=10.1109/tevc.2010.2058120\|issn=1089-778X}}</ref> and its variants<ref>{{Cite journal\|last=Iacca\|first=Giovanni\|last2=Caraffini\|first2=Fabio\|last3=Neri\|first3=Ferrante\|date=2012\|title=Compact Differential Evolution Light: High Performance Despite Limited Memory Requirement and Modest Computational Overhead\|journal=Journal of Computer Science and Technology\|language=en\|volume=27\|issue=5\|pages=1056–1076\|doi=10.1007/s11390-012-1284-2\|issn=1000-9000}}</ref><ref>{{Citation\|last=Iacca\|first=Giovanni\|title=Opposition-Based Learning in Compact Differential Evolution\|date=2011\|last2=Neri\|first2=Ferrante\|last3=Mininno\|first3=Ernesto\|work=Applications of Evolutionary Computation\|pages=264–273\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/978-3-642-20525-5_27\|isbn=9783642205248}}</ref><ref>{{Cite book\|last=Mallipeddi\|first=Rammohan\|last2=Iacca\|first2=Giovanni\|last3=Suganthan\|first3=Ponnuthurai Nagaratnam\|last4=Neri\|first4=Ferrante\|last5=Mininno\|first5=Ernesto\|date=2011\|title=Ensemble strategies in Compact Differential Evolution\|journal=2011 IEEE Congress of Evolutionary Computation (CEC)\|language=en-US\|publisher=IEEE~~\|volume=\|pages=~~\|doi=10.1109/cec.2011.5949857\|isbn=9781424478347}}</ref><ref>{{Cite journal\|last=Neri\|first=Ferrante\|last2=Iacca\|first2=Giovanni\|last3=Mininno\|first3=Ernesto\|date=2011\|title=Disturbed Exploitation compact Differential Evolution for limited memory optimization problems\|journal=Information Sciences\|volume=181\|issue=12\|pages=2469–2487\|doi=10.1016/j.ins.2011.02.004\|issn=0020-0255}}</ref><ref>{{Cite book\|last=Iacca\|first=Giovanni\|last2=Mallipeddi\|first2=Rammohan\|last3=Mininno\|first3=Ernesto\|last4=Neri\|first4=Ferrante\|last5=Suganthan\|first5=Pannuthurai Nagaratnam\|date=2011\|title=Global supervision for compact Differential Evolution\|journal=2011 IEEE Symposium on Differential Evolution (SDE)\|language=en-US\|publisher=IEEE~~\|volume=\|pages=~~\|doi=10.1109/sde.2011.5952051\|isbn=9781612840710}}</ref><ref>{{Cite book\|last=Iacca\|first=Giovanni\|last2=Mallipeddi\|first2=Rammohan\|last3=Mininno\|first3=Ernesto\|last4=Neri\|first4=Ferrante\|last5=Suganthan\|first5=Pannuthurai Nagaratnam\|date=2011\|title=Super-fit and population size reduction in compact Differential Evolution\|journal=2011 IEEE Workshop on Memetic Computing (MC)\|language=en-US\|publisher=IEEE~~\|volume=\|pages=~~\|doi=10.1109/mc.2011.5953633\|isbn=9781612840659}}</ref>		* Compact Differential Evolution (cDE)<ref>{{Cite journal\|last=Mininno\|first=Ernesto\|last2=Neri\|first2=Ferrante\|last3=Cupertino\|first3=Francesco\|last4=Naso\|first4=David\|date=2011\|title=Compact Differential Evolution\|journal=IEEE Transactions on Evolutionary Computation\|language=en-US\|volume=15\|issue=1\|pages=32–54\|doi=10.1109/tevc.2010.2058120\|issn=1089-778X}}</ref> and its variants<ref>{{Cite journal\|last=Iacca\|first=Giovanni\|last2=Caraffini\|first2=Fabio\|last3=Neri\|first3=Ferrante\|date=2012\|title=Compact Differential Evolution Light: High Performance Despite Limited Memory Requirement and Modest Computational Overhead\|journal=Journal of Computer Science and Technology\|language=en\|volume=27\|issue=5\|pages=1056–1076\|doi=10.1007/s11390-012-1284-2\|issn=1000-9000}}</ref><ref>{{Citation\|last=Iacca\|first=Giovanni\|title=Opposition-Based Learning in Compact Differential Evolution\|date=2011\|last2=Neri\|first2=Ferrante\|last3=Mininno\|first3=Ernesto\|work=Applications of Evolutionary Computation\|pages=264–273\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/978-3-642-20525-5_27\|isbn=9783642205248}}</ref><ref>{{Cite book\|last=Mallipeddi\|first=Rammohan\|last2=Iacca\|first2=Giovanni\|last3=Suganthan\|first3=Ponnuthurai Nagaratnam\|last4=Neri\|first4=Ferrante\|last5=Mininno\|first5=Ernesto\|date=2011\|title=Ensemble strategies in Compact Differential Evolution\|journal=2011 IEEE Congress of Evolutionary Computation (CEC)\|language=en-US\|publisher=IEEE\|doi=10.1109/cec.2011.5949857\|isbn=9781424478347}}</ref><ref>{{Cite journal\|last=Neri\|first=Ferrante\|last2=Iacca\|first2=Giovanni\|last3=Mininno\|first3=Ernesto\|date=2011\|title=Disturbed Exploitation compact Differential Evolution for limited memory optimization problems\|journal=Information Sciences\|volume=181\|issue=12\|pages=2469–2487\|doi=10.1016/j.ins.2011.02.004\|issn=0020-0255}}</ref><ref>{{Cite book\|last=Iacca\|first=Giovanni\|last2=Mallipeddi\|first2=Rammohan\|last3=Mininno\|first3=Ernesto\|last4=Neri\|first4=Ferrante\|last5=Suganthan\|first5=Pannuthurai Nagaratnam\|date=2011\|title=Global supervision for compact Differential Evolution\|journal=2011 IEEE Symposium on Differential Evolution (SDE)\|language=en-US\|publisher=IEEE\|doi=10.1109/sde.2011.5952051\|isbn=9781612840710}}</ref><ref>{{Cite book\|last=Iacca\|first=Giovanni\|last2=Mallipeddi\|first2=Rammohan\|last3=Mininno\|first3=Ernesto\|last4=Neri\|first4=Ferrante\|last5=Suganthan\|first5=Pannuthurai Nagaratnam\|date=2011\|title=Super-fit and population size reduction in compact Differential Evolution\|journal=2011 IEEE Workshop on Memetic Computing (MC)\|language=en-US\|publisher=IEEE\|doi=10.1109/mc.2011.5953633\|isbn=9781612840659}}</ref>
	* Compact Particle Swarm Optimization (cPSO)<ref>{{Cite journal\|last=Neri\|first=Ferrante\|last2=Mininno\|first2=Ernesto\|last3=Iacca\|first3=Giovanni\|date=2013\|title=Compact Particle Swarm Optimization\|journal=Information Sciences\|volume=239\|pages=96–121\|doi=10.1016/j.ins.2013.03.026\|issn=0020-0255}}</ref>		* Compact Particle Swarm Optimization (cPSO)<ref>{{Cite journal\|last=Neri\|first=Ferrante\|last2=Mininno\|first2=Ernesto\|last3=Iacca\|first3=Giovanni\|date=2013\|title=Compact Particle Swarm Optimization\|journal=Information Sciences\|volume=239\|pages=96–121\|doi=10.1016/j.ins.2013.03.026\|issn=0020-0255}}</ref>
	* Compact Bacterial Foraging Optimization (cBFO)<ref>{{Citation\|last=Iacca\|first=Giovanni\|title=Compact Bacterial Foraging Optimization\|date=2012\|last2=Neri\|first2=Ferrante\|last3=Mininno\|first3=Ernesto\|work=Swarm and Evolutionary Computation\|pages=84–92\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/978-3-642-29353-5_10\|isbn=9783642293528}}</ref>		* Compact Bacterial Foraging Optimization (cBFO)<ref>{{Citation\|last=Iacca\|first=Giovanni\|title=Compact Bacterial Foraging Optimization\|date=2012\|last2=Neri\|first2=Ferrante\|last3=Mininno\|first3=Ernesto\|work=Swarm and Evolutionary Computation\|pages=84–92\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/978-3-642-29353-5_10\|isbn=9783642293528}}</ref>
	* Probabilistic incremental program evolution (PIPE)<ref>{{Cite journal\|last=Salustowicz\|first=null\|last2=Schmidhuber\|first2=null\|date=1997\|title=Probabilistic incremental program evolution\|journal=Evolutionary Computation\|volume=5\|issue=2\|pages=123–141\|issn=1530-9304\|pmid=10021756\|doi=10.1162/evco.1997.5.2.123\|url=http://depositonce.tu-berlin.de/handle/11303/1046}}</ref>		* Probabilistic incremental program evolution (PIPE)<ref>{{Cite journal\|last=Salustowicz\|first=null\|last2=Schmidhuber\|first2=null\|date=1997\|title=Probabilistic incremental program evolution\|journal=Evolutionary Computation\|volume=5\|issue=2\|pages=123–141\|issn=1530-9304\|pmid=10021756\|doi=10.1162/evco.1997.5.2.123\|url=http://depositonce.tu-berlin.de/handle/11303/1046}}</ref>
	* Estimation of Gaussian networks algorithm (EGNA){{Citation needed\|date=June 2018}}		* Estimation of Gaussian networks algorithm (EGNA){{Citation needed\|date=June 2018}}
	* Estimation multivariate normal algorithm with thresheld convergence<ref>{{Cite book\|last=Tamayo-Vera\|first=Dania\|last2=Bolufe-Rohler\|first2=Antonio\|last3=Chen\|first3=Stephen\|date=2016\|title=Estimation multivariate normal algorithm with thresheld convergence\|journal=2016 IEEE Congress on Evolutionary Computation (CEC)\|language=en-US\|publisher=IEEE~~\|volume=\|pages=~~\|doi=10.1109/cec.2016.7744223\|isbn=9781509006236}}</ref>		* Estimation multivariate normal algorithm with thresheld convergence<ref>{{Cite book\|last=Tamayo-Vera\|first=Dania\|last2=Bolufe-Rohler\|first2=Antonio\|last3=Chen\|first3=Stephen\|date=2016\|title=Estimation multivariate normal algorithm with thresheld convergence\|journal=2016 IEEE Congress on Evolutionary Computation (CEC)\|language=en-US\|publisher=IEEE\|doi=10.1109/cec.2016.7744223\|isbn=9781509006236}}</ref>
	*Dependency Structure Matrix Genetic Algorithm (DSMGA)<ref>{{Citation\|last=Yu\|first=Tian-Li\|title=Genetic Algorithm Design Inspired by Organizational Theory: Pilot Study of a Dependency Structure Matrix Driven Genetic Algorithm\|date=2003\|work=Genetic and Evolutionary Computation — GECCO 2003\|pages=1620–1621\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/3-540-45110-2_54\|isbn=9783540406037\|last2=Goldberg\|first2=David E.\|last3=Yassine\|first3=Ali\|last4=Chen\|first4=Ying-Ping}}</ref><ref>{{Cite book\|last=Hsu\|first=Shih-Huan\|last2=Yu\|first2=Tian-Li\|date=2015-07-11\|title=Optimization by Pairwise Linkage Detection, Incremental Linkage Set, and Restricted / Back Mixing: DSMGA-II\|publisher=ACM\|pages=519–526\|doi=10.1145/2739480.2754737\|isbn=9781450334723\|arxiv=1807.11669}}</ref>		*Dependency Structure Matrix Genetic Algorithm (DSMGA)<ref>{{Citation\|last=Yu\|first=Tian-Li\|title=Genetic Algorithm Design Inspired by Organizational Theory: Pilot Study of a Dependency Structure Matrix Driven Genetic Algorithm\|date=2003\|work=Genetic and Evolutionary Computation — GECCO 2003\|pages=1620–1621\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/3-540-45110-2_54\|isbn=9783540406037\|last2=Goldberg\|first2=David E.\|last3=Yassine\|first3=Ali\|last4=Chen\|first4=Ying-Ping}}</ref><ref>{{Cite book\|last=Hsu\|first=Shih-Huan\|last2=Yu\|first2=Tian-Li\|date=2015-07-11\|title=Optimization by Pairwise Linkage Detection, Incremental Linkage Set, and Restricted / Back Mixing: DSMGA-II\|publisher=ACM\|pages=519–526\|doi=10.1145/2739480.2754737\|isbn=9781450334723\|arxiv=1807.11669}}</ref>

← Previous revision		Revision as of 01:15, 22 July 2023
Line 192:		Line 192:
	* Real-coded PBIL{{Citation needed\|date=June 2018}}		* Real-coded PBIL{{Citation needed\|date=June 2018}}
	* Selfish Gene Algorithm (SG)<ref>{{Cite book\|last1=Corno\|first1=Fulvio\|last2=Reorda\|first2=Matteo Sonza\|last3=Squillero\|first3=Giovanni\|date=1998-02-27\|title=The selfish gene algorithm: a new evolutionary optimization strategy\|publisher=ACM\|pages=349–355\|doi=10.1145/330560.330838\|isbn=978-0897919692\|s2cid=9125252 }}</ref>		* Selfish Gene Algorithm (SG)<ref>{{Cite book\|last1=Corno\|first1=Fulvio\|last2=Reorda\|first2=Matteo Sonza\|last3=Squillero\|first3=Giovanni\|date=1998-02-27\|title=The selfish gene algorithm: a new evolutionary optimization strategy\|publisher=ACM\|pages=349–355\|doi=10.1145/330560.330838\|isbn=978-0897919692\|s2cid=9125252 }}</ref>
	* Compact Differential Evolution (cDE)<ref>{{Cite journal\|last1=Mininno\|first1=Ernesto\|last2=Neri\|first2=Ferrante\|last3=Cupertino\|first3=Francesco\|last4=Naso\|first4=David\|date=2011\|title=Compact Differential Evolution\|journal=IEEE Transactions on Evolutionary Computation\|language=en-US\|volume=15\|issue=1\|pages=32–54\|doi=10.1109/tevc.2010.2058120\|s2cid=20582233 \|issn=1089-778X}}</ref> and its variants<ref>{{Cite journal\|last1=Iacca\|first1=Giovanni\|last2=Caraffini\|first2=Fabio\|last3=Neri\|first3=Ferrante\|date=2012\|title=Compact Differential Evolution Light: High Performance Despite Limited Memory Requirement and Modest Computational Overhead\|journal=Journal of Computer Science and Technology\|language=en\|volume=27\|issue=5\|pages=1056–1076\|doi=10.1007/s11390-012-1284-2\|s2cid=3184035 \|issn=1000-9000}}</ref><ref>{{Citation\|last1=Iacca\|first1=Giovanni\|title=Opposition-Based Learning in Compact Differential Evolution\|date=2011\|last2=Neri\|first2=Ferrante\|last3=Mininno\|first3=Ernesto\|work=Applications of Evolutionary Computation\|pages=264–273\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/978-3-642-20525-5_27\|isbn=9783642205248}}</ref><ref>{{Cite book\|last1=Mallipeddi\|first1=Rammohan\|last2=Iacca\|first2=Giovanni\|last3=Suganthan\|first3=Ponnuthurai Nagaratnam\|last4=Neri\|first4=Ferrante\|last5=Mininno\|first5=Ernesto\|~~date~~=2011\|~~title~~=Ensemble strategies in Compact Differential Evolution\|~~journal~~=2011 ~~IEEE Congress of Evolutionary Computation (CEC)~~\|language=en-US\|publisher=IEEE\|doi=10.1109/cec.2011.5949857\|isbn=9781424478347\|s2cid=11781300 }}</ref><ref>{{Cite journal\|last1=Neri\|first1=Ferrante\|last2=Iacca\|first2=Giovanni\|last3=Mininno\|first3=Ernesto\|date=2011\|title=Disturbed Exploitation compact Differential Evolution for limited memory optimization problems\|journal=Information Sciences\|volume=181\|issue=12\|pages=2469–2487\|doi=10.1016/j.ins.2011.02.004\|issn=0020-0255}}</ref><ref>{{Cite book\|last1=Iacca\|first1=Giovanni\|last2=Mallipeddi\|first2=Rammohan\|last3=Mininno\|first3=Ernesto\|last4=Neri\|first4=Ferrante\|last5=Suganthan\|first5=Pannuthurai Nagaratnam~~\|date=2011~~\|title=~~Global~~ ~~supervision~~ ~~for~~ ~~compact~~ Differential Evolution\|~~journal~~=~~2011~~ ~~IEEE~~ ~~Symposium~~ on Differential Evolution ~~(SDE)~~\|language=en-US\|publisher=IEEE\|doi=10.1109/sde.2011.5952051\|isbn=9781612840710\|s2cid=8874851 }}</ref><ref>{{Cite book\|last1=Iacca\|first1=Giovanni\|last2=Mallipeddi\|first2=Rammohan\|last3=Mininno\|first3=Ernesto\|last4=Neri\|first4=Ferrante\|last5=Suganthan\|first5=Pannuthurai Nagaratnam\|~~date~~=2011\|~~title~~=Super-fit and population size reduction in compact Differential Evolution\|~~journal~~=2011 ~~IEEE Workshop on Memetic Computing (MC)~~\|language=en-US\|publisher=IEEE\|doi=10.1109/mc.2011.5953633\|isbn=9781612840659\|s2cid=5692951 }}</ref>		* Compact Differential Evolution (cDE)<ref>{{Cite journal\|last1=Mininno\|first1=Ernesto\|last2=Neri\|first2=Ferrante\|last3=Cupertino\|first3=Francesco\|last4=Naso\|first4=David\|date=2011\|title=Compact Differential Evolution\|journal=IEEE Transactions on Evolutionary Computation\|language=en-US\|volume=15\|issue=1\|pages=32–54\|doi=10.1109/tevc.2010.2058120\|s2cid=20582233 \|issn=1089-778X}}</ref> and its variants<ref>{{Cite journal\|last1=Iacca\|first1=Giovanni\|last2=Caraffini\|first2=Fabio\|last3=Neri\|first3=Ferrante\|date=2012\|title=Compact Differential Evolution Light: High Performance Despite Limited Memory Requirement and Modest Computational Overhead\|journal=Journal of Computer Science and Technology\|language=en\|volume=27\|issue=5\|pages=1056–1076\|doi=10.1007/s11390-012-1284-2\|s2cid=3184035 \|issn=1000-9000}}</ref><ref>{{Citation\|last1=Iacca\|first1=Giovanni\|title=Opposition-Based Learning in Compact Differential Evolution\|date=2011\|last2=Neri\|first2=Ferrante\|last3=Mininno\|first3=Ernesto\|work=Applications of Evolutionary Computation\|pages=264–273\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/978-3-642-20525-5_27\|isbn=9783642205248}}</ref><ref>{{Cite book\|last1=Mallipeddi\|first1=Rammohan\|last2=Iacca\|first2=Giovanni\|last3=Suganthan\|first3=Ponnuthurai Nagaratnam\|last4=Neri\|first4=Ferrante\|last5=Mininno\|first5=Ernesto\|title=2011 IEEE Congress of Evolutionary Computation (CEC) \|chapter=Ensemble strategies in Compact Differential Evolution \|date=2011\|pages=1972–1977 \|language=en-US\|publisher=IEEE\|doi=10.1109/cec.2011.5949857\|isbn=9781424478347\|s2cid=11781300 }}</ref><ref>{{Cite journal\|last1=Neri\|first1=Ferrante\|last2=Iacca\|first2=Giovanni\|last3=Mininno\|first3=Ernesto\|date=2011\|title=Disturbed Exploitation compact Differential Evolution for limited memory optimization problems\|journal=Information Sciences\|volume=181\|issue=12\|pages=2469–2487\|doi=10.1016/j.ins.2011.02.004\|issn=0020-0255}}</ref><ref>{{Cite book\|last1=Iacca\|first1=Giovanni\|last2=Mallipeddi\|first2=Rammohan\|last3=Mininno\|first3=Ernesto\|last4=Neri\|first4=Ferrante\|last5=Suganthan\|first5=Pannuthurai Nagaratnam\|title=2011 IEEE Symposium on Differential Evolution (SDE) \|chapter=Global supervision for compact Differential Evolution \|date=2011\|pages=1–8 \|language=en-US\|publisher=IEEE\|doi=10.1109/sde.2011.5952051\|isbn=9781612840710\|s2cid=8874851 }}</ref><ref>{{Cite book\|last1=Iacca\|first1=Giovanni\|last2=Mallipeddi\|first2=Rammohan\|last3=Mininno\|first3=Ernesto\|last4=Neri\|first4=Ferrante\|last5=Suganthan\|first5=Pannuthurai Nagaratnam\|title=2011 IEEE Workshop on Memetic Computing (MC) \|chapter=Super-fit and population size reduction in compact Differential Evolution \|date=2011\|pages=1–8 \|language=en-US\|publisher=IEEE\|doi=10.1109/mc.2011.5953633\|isbn=9781612840659\|s2cid=5692951 }}</ref>
	* Compact Particle Swarm Optimization (cPSO)<ref>{{Cite journal\|last1=Neri\|first1=Ferrante\|last2=Mininno\|first2=Ernesto\|last3=Iacca\|first3=Giovanni\|date=2013\|title=Compact Particle Swarm Optimization\|journal=Information Sciences\|volume=239\|pages=96–121\|doi=10.1016/j.ins.2013.03.026\|issn=0020-0255}}</ref>		* Compact Particle Swarm Optimization (cPSO)<ref>{{Cite journal\|last1=Neri\|first1=Ferrante\|last2=Mininno\|first2=Ernesto\|last3=Iacca\|first3=Giovanni\|date=2013\|title=Compact Particle Swarm Optimization\|journal=Information Sciences\|volume=239\|pages=96–121\|doi=10.1016/j.ins.2013.03.026\|issn=0020-0255}}</ref>
	* Compact Bacterial Foraging Optimization (cBFO)<ref>{{Citation\|last1=Iacca\|first1=Giovanni\|title=Compact Bacterial Foraging Optimization\|date=2012\|last2=Neri\|first2=Ferrante\|last3=Mininno\|first3=Ernesto\|work=Swarm and Evolutionary Computation\|pages=84–92\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/978-3-642-29353-5_10\|hdl=11572/196442 \|isbn=9783642293528}}</ref>		* Compact Bacterial Foraging Optimization (cBFO)<ref>{{Citation\|last1=Iacca\|first1=Giovanni\|title=Compact Bacterial Foraging Optimization\|date=2012\|last2=Neri\|first2=Ferrante\|last3=Mininno\|first3=Ernesto\|work=Swarm and Evolutionary Computation\|pages=84–92\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/978-3-642-29353-5_10\|hdl=11572/196442 \|isbn=9783642293528}}</ref>
	* Probabilistic incremental program evolution (PIPE)<ref>{{Cite journal\|last1=Salustowicz\|first1=null\|last2=Schmidhuber\|first2=null\|date=1997\|title=Probabilistic incremental program evolution\|journal=Evolutionary Computation\|volume=5\|issue=2\|pages=123–141\|issn=1530-9304\|pmid=10021756\|doi=10.1162/evco.1997.5.2.123\|s2cid=10759266 \|url=http://depositonce.tu-berlin.de/handle/11303/1046}}</ref>		* Probabilistic incremental program evolution (PIPE)<ref>{{Cite journal\|last1=Salustowicz\|first1=null\|last2=Schmidhuber\|first2=null\|date=1997\|title=Probabilistic incremental program evolution\|journal=Evolutionary Computation\|volume=5\|issue=2\|pages=123–141\|issn=1530-9304\|pmid=10021756\|doi=10.1162/evco.1997.5.2.123\|s2cid=10759266 \|url=http://depositonce.tu-berlin.de/handle/11303/1046}}</ref>
	* Estimation of Gaussian networks algorithm (EGNA){{Citation needed\|date=June 2018}}		* Estimation of Gaussian networks algorithm (EGNA){{Citation needed\|date=June 2018}}
	* Estimation multivariate normal algorithm with thresheld convergence<ref>{{Cite book\|last1=Tamayo-Vera\|first1=Dania\|last2=Bolufe-Rohler\|first2=Antonio\|last3=Chen\|first3=Stephen\|~~date~~=2016\|~~title~~=Estimation multivariate normal algorithm with thresheld convergence\|~~journal~~=2016 ~~IEEE Congress on Evolutionary Computation (CEC)~~\|language=en-US\|publisher=IEEE\|doi=10.1109/cec.2016.7744223\|isbn=9781509006236\|s2cid=33114730 }}</ref>		* Estimation multivariate normal algorithm with thresheld convergence<ref>{{Cite book\|last1=Tamayo-Vera\|first1=Dania\|last2=Bolufe-Rohler\|first2=Antonio\|last3=Chen\|first3=Stephen\|title=2016 IEEE Congress on Evolutionary Computation (CEC) \|chapter=Estimation multivariate normal algorithm with thresheld convergence \|date=2016\|pages=3425–3432 \|language=en-US\|publisher=IEEE\|doi=10.1109/cec.2016.7744223\|isbn=9781509006236\|s2cid=33114730 }}</ref>
	*Dependency Structure Matrix Genetic Algorithm (DSMGA)<ref>{{Citation\|last1=Yu\|first1=Tian-Li\|title=Genetic Algorithm Design Inspired by Organizational Theory: Pilot Study of a Dependency Structure Matrix Driven Genetic Algorithm\|date=2003\|work=Genetic and Evolutionary Computation — GECCO 2003\|pages=1620–1621\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/3-540-45110-2_54\|isbn=9783540406037\|last2=Goldberg\|first2=David E.\|last3=Yassine\|first3=Ali\|last4=Chen\|first4=Ying-Ping}}</ref><ref>{{Cite book\|last1=Hsu\|first1=Shih-Huan\|last2=Yu\|first2=Tian-Li\|date=2015-07-11\|title=Optimization by Pairwise Linkage Detection, Incremental Linkage Set, and Restricted / Back Mixing: DSMGA-II\|publisher=ACM\|pages=519–526\|doi=10.1145/2739480.2754737\|isbn=9781450334723\|arxiv=1807.11669\|s2cid=17031156 }}</ref>		*Dependency Structure Matrix Genetic Algorithm (DSMGA)<ref>{{Citation\|last1=Yu\|first1=Tian-Li\|title=Genetic Algorithm Design Inspired by Organizational Theory: Pilot Study of a Dependency Structure Matrix Driven Genetic Algorithm\|date=2003\|work=Genetic and Evolutionary Computation — GECCO 2003\|pages=1620–1621\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/3-540-45110-2_54\|isbn=9783540406037\|last2=Goldberg\|first2=David E.\|last3=Yassine\|first3=Ali\|last4=Chen\|first4=Ying-Ping}}</ref><ref>{{Cite book\|last1=Hsu\|first1=Shih-Huan\|last2=Yu\|first2=Tian-Li\|date=2015-07-11\|title=Optimization by Pairwise Linkage Detection, Incremental Linkage Set, and Restricted / Back Mixing: DSMGA-II\|publisher=ACM\|pages=519–526\|doi=10.1145/2739480.2754737\|isbn=9781450334723\|arxiv=1807.11669\|s2cid=17031156 }}</ref>

← Previous revision		Revision as of 10:52, 11 February 2023
Line 194:		Line 194:
	* Compact Differential Evolution (cDE)<ref>{{Cite journal\|last1=Mininno\|first1=Ernesto\|last2=Neri\|first2=Ferrante\|last3=Cupertino\|first3=Francesco\|last4=Naso\|first4=David\|date=2011\|title=Compact Differential Evolution\|journal=IEEE Transactions on Evolutionary Computation\|language=en-US\|volume=15\|issue=1\|pages=32–54\|doi=10.1109/tevc.2010.2058120\|s2cid=20582233 \|issn=1089-778X}}</ref> and its variants<ref>{{Cite journal\|last1=Iacca\|first1=Giovanni\|last2=Caraffini\|first2=Fabio\|last3=Neri\|first3=Ferrante\|date=2012\|title=Compact Differential Evolution Light: High Performance Despite Limited Memory Requirement and Modest Computational Overhead\|journal=Journal of Computer Science and Technology\|language=en\|volume=27\|issue=5\|pages=1056–1076\|doi=10.1007/s11390-012-1284-2\|s2cid=3184035 \|issn=1000-9000}}</ref><ref>{{Citation\|last1=Iacca\|first1=Giovanni\|title=Opposition-Based Learning in Compact Differential Evolution\|date=2011\|last2=Neri\|first2=Ferrante\|last3=Mininno\|first3=Ernesto\|work=Applications of Evolutionary Computation\|pages=264–273\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/978-3-642-20525-5_27\|isbn=9783642205248}}</ref><ref>{{Cite book\|last1=Mallipeddi\|first1=Rammohan\|last2=Iacca\|first2=Giovanni\|last3=Suganthan\|first3=Ponnuthurai Nagaratnam\|last4=Neri\|first4=Ferrante\|last5=Mininno\|first5=Ernesto\|date=2011\|title=Ensemble strategies in Compact Differential Evolution\|journal=2011 IEEE Congress of Evolutionary Computation (CEC)\|language=en-US\|publisher=IEEE\|doi=10.1109/cec.2011.5949857\|isbn=9781424478347\|s2cid=11781300 }}</ref><ref>{{Cite journal\|last1=Neri\|first1=Ferrante\|last2=Iacca\|first2=Giovanni\|last3=Mininno\|first3=Ernesto\|date=2011\|title=Disturbed Exploitation compact Differential Evolution for limited memory optimization problems\|journal=Information Sciences\|volume=181\|issue=12\|pages=2469–2487\|doi=10.1016/j.ins.2011.02.004\|issn=0020-0255}}</ref><ref>{{Cite book\|last1=Iacca\|first1=Giovanni\|last2=Mallipeddi\|first2=Rammohan\|last3=Mininno\|first3=Ernesto\|last4=Neri\|first4=Ferrante\|last5=Suganthan\|first5=Pannuthurai Nagaratnam\|date=2011\|title=Global supervision for compact Differential Evolution\|journal=2011 IEEE Symposium on Differential Evolution (SDE)\|language=en-US\|publisher=IEEE\|doi=10.1109/sde.2011.5952051\|isbn=9781612840710\|s2cid=8874851 }}</ref><ref>{{Cite book\|last1=Iacca\|first1=Giovanni\|last2=Mallipeddi\|first2=Rammohan\|last3=Mininno\|first3=Ernesto\|last4=Neri\|first4=Ferrante\|last5=Suganthan\|first5=Pannuthurai Nagaratnam\|date=2011\|title=Super-fit and population size reduction in compact Differential Evolution\|journal=2011 IEEE Workshop on Memetic Computing (MC)\|language=en-US\|publisher=IEEE\|doi=10.1109/mc.2011.5953633\|isbn=9781612840659\|s2cid=5692951 }}</ref>		* Compact Differential Evolution (cDE)<ref>{{Cite journal\|last1=Mininno\|first1=Ernesto\|last2=Neri\|first2=Ferrante\|last3=Cupertino\|first3=Francesco\|last4=Naso\|first4=David\|date=2011\|title=Compact Differential Evolution\|journal=IEEE Transactions on Evolutionary Computation\|language=en-US\|volume=15\|issue=1\|pages=32–54\|doi=10.1109/tevc.2010.2058120\|s2cid=20582233 \|issn=1089-778X}}</ref> and its variants<ref>{{Cite journal\|last1=Iacca\|first1=Giovanni\|last2=Caraffini\|first2=Fabio\|last3=Neri\|first3=Ferrante\|date=2012\|title=Compact Differential Evolution Light: High Performance Despite Limited Memory Requirement and Modest Computational Overhead\|journal=Journal of Computer Science and Technology\|language=en\|volume=27\|issue=5\|pages=1056–1076\|doi=10.1007/s11390-012-1284-2\|s2cid=3184035 \|issn=1000-9000}}</ref><ref>{{Citation\|last1=Iacca\|first1=Giovanni\|title=Opposition-Based Learning in Compact Differential Evolution\|date=2011\|last2=Neri\|first2=Ferrante\|last3=Mininno\|first3=Ernesto\|work=Applications of Evolutionary Computation\|pages=264–273\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/978-3-642-20525-5_27\|isbn=9783642205248}}</ref><ref>{{Cite book\|last1=Mallipeddi\|first1=Rammohan\|last2=Iacca\|first2=Giovanni\|last3=Suganthan\|first3=Ponnuthurai Nagaratnam\|last4=Neri\|first4=Ferrante\|last5=Mininno\|first5=Ernesto\|date=2011\|title=Ensemble strategies in Compact Differential Evolution\|journal=2011 IEEE Congress of Evolutionary Computation (CEC)\|language=en-US\|publisher=IEEE\|doi=10.1109/cec.2011.5949857\|isbn=9781424478347\|s2cid=11781300 }}</ref><ref>{{Cite journal\|last1=Neri\|first1=Ferrante\|last2=Iacca\|first2=Giovanni\|last3=Mininno\|first3=Ernesto\|date=2011\|title=Disturbed Exploitation compact Differential Evolution for limited memory optimization problems\|journal=Information Sciences\|volume=181\|issue=12\|pages=2469–2487\|doi=10.1016/j.ins.2011.02.004\|issn=0020-0255}}</ref><ref>{{Cite book\|last1=Iacca\|first1=Giovanni\|last2=Mallipeddi\|first2=Rammohan\|last3=Mininno\|first3=Ernesto\|last4=Neri\|first4=Ferrante\|last5=Suganthan\|first5=Pannuthurai Nagaratnam\|date=2011\|title=Global supervision for compact Differential Evolution\|journal=2011 IEEE Symposium on Differential Evolution (SDE)\|language=en-US\|publisher=IEEE\|doi=10.1109/sde.2011.5952051\|isbn=9781612840710\|s2cid=8874851 }}</ref><ref>{{Cite book\|last1=Iacca\|first1=Giovanni\|last2=Mallipeddi\|first2=Rammohan\|last3=Mininno\|first3=Ernesto\|last4=Neri\|first4=Ferrante\|last5=Suganthan\|first5=Pannuthurai Nagaratnam\|date=2011\|title=Super-fit and population size reduction in compact Differential Evolution\|journal=2011 IEEE Workshop on Memetic Computing (MC)\|language=en-US\|publisher=IEEE\|doi=10.1109/mc.2011.5953633\|isbn=9781612840659\|s2cid=5692951 }}</ref>
	* Compact Particle Swarm Optimization (cPSO)<ref>{{Cite journal\|last1=Neri\|first1=Ferrante\|last2=Mininno\|first2=Ernesto\|last3=Iacca\|first3=Giovanni\|date=2013\|title=Compact Particle Swarm Optimization\|journal=Information Sciences\|volume=239\|pages=96–121\|doi=10.1016/j.ins.2013.03.026\|issn=0020-0255}}</ref>		* Compact Particle Swarm Optimization (cPSO)<ref>{{Cite journal\|last1=Neri\|first1=Ferrante\|last2=Mininno\|first2=Ernesto\|last3=Iacca\|first3=Giovanni\|date=2013\|title=Compact Particle Swarm Optimization\|journal=Information Sciences\|volume=239\|pages=96–121\|doi=10.1016/j.ins.2013.03.026\|issn=0020-0255}}</ref>
	* Compact Bacterial Foraging Optimization (cBFO)<ref>{{Citation\|last1=Iacca\|first1=Giovanni\|title=Compact Bacterial Foraging Optimization\|date=2012\|last2=Neri\|first2=Ferrante\|last3=Mininno\|first3=Ernesto\|work=Swarm and Evolutionary Computation\|pages=84–92\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/978-3-642-29353-5_10\|isbn=9783642293528}}</ref>		* Compact Bacterial Foraging Optimization (cBFO)<ref>{{Citation\|last1=Iacca\|first1=Giovanni\|title=Compact Bacterial Foraging Optimization\|date=2012\|last2=Neri\|first2=Ferrante\|last3=Mininno\|first3=Ernesto\|work=Swarm and Evolutionary Computation\|pages=84–92\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/978-3-642-29353-5_10\|hdl=11572/196442 \|isbn=9783642293528}}</ref>
	* Probabilistic incremental program evolution (PIPE)<ref>{{Cite journal\|last1=Salustowicz\|first1=null\|last2=Schmidhuber\|first2=null\|date=1997\|title=Probabilistic incremental program evolution\|journal=Evolutionary Computation\|volume=5\|issue=2\|pages=123–141\|issn=1530-9304\|pmid=10021756\|doi=10.1162/evco.1997.5.2.123\|s2cid=10759266 \|url=http://depositonce.tu-berlin.de/handle/11303/1046}}</ref>		* Probabilistic incremental program evolution (PIPE)<ref>{{Cite journal\|last1=Salustowicz\|first1=null\|last2=Schmidhuber\|first2=null\|date=1997\|title=Probabilistic incremental program evolution\|journal=Evolutionary Computation\|volume=5\|issue=2\|pages=123–141\|issn=1530-9304\|pmid=10021756\|doi=10.1162/evco.1997.5.2.123\|s2cid=10759266 \|url=http://depositonce.tu-berlin.de/handle/11303/1046}}</ref>
	* Estimation of Gaussian networks algorithm (EGNA){{Citation needed\|date=June 2018}}		* Estimation of Gaussian networks algorithm (EGNA){{Citation needed\|date=June 2018}}

← Previous revision		Revision as of 22:38, 17 December 2020
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	* Real-coded PBIL{{Citation needed\|date=June 2018}}		* Real-coded PBIL{{Citation needed\|date=June 2018}}
	* Selfish Gene Algorithm (SG)<ref>{{Cite book\|last=Corno\|first=Fulvio\|last2=Reorda\|first2=Matteo Sonza\|last3=Squillero\|first3=Giovanni\|date=1998-02-27\|title=The selfish gene algorithm: a new evolutionary optimization strategy\|publisher=ACM\|pages=349–355\|doi=10.1145/330560.330838\|isbn=978-0897919692}}</ref>		* Selfish Gene Algorithm (SG)<ref>{{Cite book\|last=Corno\|first=Fulvio\|last2=Reorda\|first2=Matteo Sonza\|last3=Squillero\|first3=Giovanni\|date=1998-02-27\|title=The selfish gene algorithm: a new evolutionary optimization strategy\|publisher=ACM\|pages=349–355\|doi=10.1145/330560.330838\|isbn=978-0897919692}}</ref>
	* Compact Differential Evolution (cDE)<ref>{{Cite journal\|last=Mininno\|first=Ernesto\|last2=Neri\|first2=Ferrante\|last3=Cupertino\|first3=Francesco\|last4=Naso\|first4=David\|date=2011\|title=Compact Differential Evolution\|journal=IEEE Transactions on Evolutionary Computation\|language=en-US\|volume=15\|issue=1\|pages=32–54\|doi=10.1109/tevc.2010.2058120\|issn=1089-778X}}</ref> and its variants<ref>{{Cite journal\|last=Iacca\|first=Giovanni\|last2=Caraffini\|first2=Fabio\|last3=Neri\|first3=Ferrante\|date=2012\|title=Compact Differential Evolution Light: High Performance Despite Limited Memory Requirement and Modest Computational Overhead\|journal=Journal of Computer Science and Technology\|language=en\|volume=27\|issue=5\|pages=1056–1076\|doi=10.1007/s11390-012-1284-2\|issn=1000-9000}}</ref><ref>{{Citation\|last=Iacca\|first=Giovanni\|title=Opposition-Based Learning in Compact Differential Evolution\|date=2011\|last2=Neri\|first2=Ferrante\|last3=Mininno\|first3=Ernesto\|work=Applications of Evolutionary Computation\|pages=264–273\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/978-3-642-20525-5_27\|isbn=9783642205248}}</ref><ref>{{Cite book\|last=Mallipeddi\|first=Rammohan\|last2=Iacca\|first2=Giovanni\|last3=Suganthan\|first3=Ponnuthurai Nagaratnam\|last4=Neri\|first4=Ferrante\|last5=Mininno\|first5=Ernesto\|date=2011\|title=Ensemble strategies in Compact Differential Evolution\|journal=2011 IEEE Congress of Evolutionary Computation (CEC)\|language=en-US\|publisher=IEEE~~\|volume=\|pages=~~\|doi=10.1109/cec.2011.5949857\|isbn=9781424478347}}</ref><ref>{{Cite journal\|last=Neri\|first=Ferrante\|last2=Iacca\|first2=Giovanni\|last3=Mininno\|first3=Ernesto\|date=2011\|title=Disturbed Exploitation compact Differential Evolution for limited memory optimization problems\|journal=Information Sciences\|volume=181\|issue=12\|pages=2469–2487\|doi=10.1016/j.ins.2011.02.004\|issn=0020-0255}}</ref><ref>{{Cite book\|last=Iacca\|first=Giovanni\|last2=Mallipeddi\|first2=Rammohan\|last3=Mininno\|first3=Ernesto\|last4=Neri\|first4=Ferrante\|last5=Suganthan\|first5=Pannuthurai Nagaratnam\|date=2011\|title=Global supervision for compact Differential Evolution\|journal=2011 IEEE Symposium on Differential Evolution (SDE)\|language=en-US\|publisher=IEEE~~\|volume=\|pages=~~\|doi=10.1109/sde.2011.5952051\|isbn=9781612840710}}</ref><ref>{{Cite book\|last=Iacca\|first=Giovanni\|last2=Mallipeddi\|first2=Rammohan\|last3=Mininno\|first3=Ernesto\|last4=Neri\|first4=Ferrante\|last5=Suganthan\|first5=Pannuthurai Nagaratnam\|date=2011\|title=Super-fit and population size reduction in compact Differential Evolution\|journal=2011 IEEE Workshop on Memetic Computing (MC)\|language=en-US\|publisher=IEEE~~\|volume=\|pages=~~\|doi=10.1109/mc.2011.5953633\|isbn=9781612840659}}</ref>		* Compact Differential Evolution (cDE)<ref>{{Cite journal\|last=Mininno\|first=Ernesto\|last2=Neri\|first2=Ferrante\|last3=Cupertino\|first3=Francesco\|last4=Naso\|first4=David\|date=2011\|title=Compact Differential Evolution\|journal=IEEE Transactions on Evolutionary Computation\|language=en-US\|volume=15\|issue=1\|pages=32–54\|doi=10.1109/tevc.2010.2058120\|issn=1089-778X}}</ref> and its variants<ref>{{Cite journal\|last=Iacca\|first=Giovanni\|last2=Caraffini\|first2=Fabio\|last3=Neri\|first3=Ferrante\|date=2012\|title=Compact Differential Evolution Light: High Performance Despite Limited Memory Requirement and Modest Computational Overhead\|journal=Journal of Computer Science and Technology\|language=en\|volume=27\|issue=5\|pages=1056–1076\|doi=10.1007/s11390-012-1284-2\|issn=1000-9000}}</ref><ref>{{Citation\|last=Iacca\|first=Giovanni\|title=Opposition-Based Learning in Compact Differential Evolution\|date=2011\|last2=Neri\|first2=Ferrante\|last3=Mininno\|first3=Ernesto\|work=Applications of Evolutionary Computation\|pages=264–273\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/978-3-642-20525-5_27\|isbn=9783642205248}}</ref><ref>{{Cite book\|last=Mallipeddi\|first=Rammohan\|last2=Iacca\|first2=Giovanni\|last3=Suganthan\|first3=Ponnuthurai Nagaratnam\|last4=Neri\|first4=Ferrante\|last5=Mininno\|first5=Ernesto\|date=2011\|title=Ensemble strategies in Compact Differential Evolution\|journal=2011 IEEE Congress of Evolutionary Computation (CEC)\|language=en-US\|publisher=IEEE\|doi=10.1109/cec.2011.5949857\|isbn=9781424478347}}</ref><ref>{{Cite journal\|last=Neri\|first=Ferrante\|last2=Iacca\|first2=Giovanni\|last3=Mininno\|first3=Ernesto\|date=2011\|title=Disturbed Exploitation compact Differential Evolution for limited memory optimization problems\|journal=Information Sciences\|volume=181\|issue=12\|pages=2469–2487\|doi=10.1016/j.ins.2011.02.004\|issn=0020-0255}}</ref><ref>{{Cite book\|last=Iacca\|first=Giovanni\|last2=Mallipeddi\|first2=Rammohan\|last3=Mininno\|first3=Ernesto\|last4=Neri\|first4=Ferrante\|last5=Suganthan\|first5=Pannuthurai Nagaratnam\|date=2011\|title=Global supervision for compact Differential Evolution\|journal=2011 IEEE Symposium on Differential Evolution (SDE)\|language=en-US\|publisher=IEEE\|doi=10.1109/sde.2011.5952051\|isbn=9781612840710}}</ref><ref>{{Cite book\|last=Iacca\|first=Giovanni\|last2=Mallipeddi\|first2=Rammohan\|last3=Mininno\|first3=Ernesto\|last4=Neri\|first4=Ferrante\|last5=Suganthan\|first5=Pannuthurai Nagaratnam\|date=2011\|title=Super-fit and population size reduction in compact Differential Evolution\|journal=2011 IEEE Workshop on Memetic Computing (MC)\|language=en-US\|publisher=IEEE\|doi=10.1109/mc.2011.5953633\|isbn=9781612840659}}</ref>
	* Compact Particle Swarm Optimization (cPSO)<ref>{{Cite journal\|last=Neri\|first=Ferrante\|last2=Mininno\|first2=Ernesto\|last3=Iacca\|first3=Giovanni\|date=2013\|title=Compact Particle Swarm Optimization\|journal=Information Sciences\|volume=239\|pages=96–121\|doi=10.1016/j.ins.2013.03.026\|issn=0020-0255}}</ref>		* Compact Particle Swarm Optimization (cPSO)<ref>{{Cite journal\|last=Neri\|first=Ferrante\|last2=Mininno\|first2=Ernesto\|last3=Iacca\|first3=Giovanni\|date=2013\|title=Compact Particle Swarm Optimization\|journal=Information Sciences\|volume=239\|pages=96–121\|doi=10.1016/j.ins.2013.03.026\|issn=0020-0255}}</ref>
	* Compact Bacterial Foraging Optimization (cBFO)<ref>{{Citation\|last=Iacca\|first=Giovanni\|title=Compact Bacterial Foraging Optimization\|date=2012\|last2=Neri\|first2=Ferrante\|last3=Mininno\|first3=Ernesto\|work=Swarm and Evolutionary Computation\|pages=84–92\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/978-3-642-29353-5_10\|isbn=9783642293528}}</ref>		* Compact Bacterial Foraging Optimization (cBFO)<ref>{{Citation\|last=Iacca\|first=Giovanni\|title=Compact Bacterial Foraging Optimization\|date=2012\|last2=Neri\|first2=Ferrante\|last3=Mininno\|first3=Ernesto\|work=Swarm and Evolutionary Computation\|pages=84–92\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/978-3-642-29353-5_10\|isbn=9783642293528}}</ref>
	* Probabilistic incremental program evolution (PIPE)<ref>{{Cite journal\|last=Salustowicz\|first=null\|last2=Schmidhuber\|first2=null\|date=1997\|title=Probabilistic incremental program evolution\|journal=Evolutionary Computation\|volume=5\|issue=2\|pages=123–141\|issn=1530-9304\|pmid=10021756\|doi=10.1162/evco.1997.5.2.123\|url=http://depositonce.tu-berlin.de/handle/11303/1046}}</ref>		* Probabilistic incremental program evolution (PIPE)<ref>{{Cite journal\|last=Salustowicz\|first=null\|last2=Schmidhuber\|first2=null\|date=1997\|title=Probabilistic incremental program evolution\|journal=Evolutionary Computation\|volume=5\|issue=2\|pages=123–141\|issn=1530-9304\|pmid=10021756\|doi=10.1162/evco.1997.5.2.123\|url=http://depositonce.tu-berlin.de/handle/11303/1046}}</ref>
	* Estimation of Gaussian networks algorithm (EGNA){{Citation needed\|date=June 2018}}		* Estimation of Gaussian networks algorithm (EGNA){{Citation needed\|date=June 2018}}
	* Estimation multivariate normal algorithm with thresheld convergence<ref>{{Cite book\|last=Tamayo-Vera\|first=Dania\|last2=Bolufe-Rohler\|first2=Antonio\|last3=Chen\|first3=Stephen\|date=2016\|title=Estimation multivariate normal algorithm with thresheld convergence\|journal=2016 IEEE Congress on Evolutionary Computation (CEC)\|language=en-US\|publisher=IEEE~~\|volume=\|pages=~~\|doi=10.1109/cec.2016.7744223\|isbn=9781509006236}}</ref>		* Estimation multivariate normal algorithm with thresheld convergence<ref>{{Cite book\|last=Tamayo-Vera\|first=Dania\|last2=Bolufe-Rohler\|first2=Antonio\|last3=Chen\|first3=Stephen\|date=2016\|title=Estimation multivariate normal algorithm with thresheld convergence\|journal=2016 IEEE Congress on Evolutionary Computation (CEC)\|language=en-US\|publisher=IEEE\|doi=10.1109/cec.2016.7744223\|isbn=9781509006236}}</ref>
	*Dependency Structure Matrix Genetic Algorithm (DSMGA)<ref>{{Citation\|last=Yu\|first=Tian-Li\|title=Genetic Algorithm Design Inspired by Organizational Theory: Pilot Study of a Dependency Structure Matrix Driven Genetic Algorithm\|date=2003\|work=Genetic and Evolutionary Computation — GECCO 2003\|pages=1620–1621\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/3-540-45110-2_54\|isbn=9783540406037\|last2=Goldberg\|first2=David E.\|last3=Yassine\|first3=Ali\|last4=Chen\|first4=Ying-Ping}}</ref><ref>{{Cite book\|last=Hsu\|first=Shih-Huan\|last2=Yu\|first2=Tian-Li\|date=2015-07-11\|title=Optimization by Pairwise Linkage Detection, Incremental Linkage Set, and Restricted / Back Mixing: DSMGA-II\|publisher=ACM\|pages=519–526\|doi=10.1145/2739480.2754737\|isbn=9781450334723\|arxiv=1807.11669}}</ref>		*Dependency Structure Matrix Genetic Algorithm (DSMGA)<ref>{{Citation\|last=Yu\|first=Tian-Li\|title=Genetic Algorithm Design Inspired by Organizational Theory: Pilot Study of a Dependency Structure Matrix Driven Genetic Algorithm\|date=2003\|work=Genetic and Evolutionary Computation — GECCO 2003\|pages=1620–1621\|publisher=Springer Berlin Heidelberg\|language=en\|doi=10.1007/3-540-45110-2_54\|isbn=9783540406037\|last2=Goldberg\|first2=David E.\|last3=Yassine\|first3=Ali\|last4=Chen\|first4=Ying-Ping}}</ref><ref>{{Cite book\|last=Hsu\|first=Shih-Huan\|last2=Yu\|first2=Tian-Li\|date=2015-07-11\|title=Optimization by Pairwise Linkage Detection, Incremental Linkage Set, and Restricted / Back Mixing: DSMGA-II\|publisher=ACM\|pages=519–526\|doi=10.1145/2739480.2754737\|isbn=9781450334723\|arxiv=1807.11669}}</ref>