Model-based evolutionary algorithms: a short survey

Abstract The evolutionary algorithms (EAs) are a family of nature-inspired algorithms widely used for solving complex optimization problems. Since the operators (e.g. crossover, mutation, selection) in most traditional EAs are developed on the basis of fixed heuristic rules or strategies, they are u...
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Autor*in:	Cheng, Ran [verfasserIn] He, Cheng Jin, Yaochu Yao, Xin

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Model-based evolutionary algorithms Estimation of distribution algorithms Surrogate modelling Inverse modelling

Anmerkung:	© The Author(s) 2018

Übergeordnetes Werk:	Enthalten in: Complex & intelligent systems - Berlin : SpringerOpen, 2015, 4(2018), 4 vom: 07. Aug., Seite 283-292
Übergeordnetes Werk:	volume:4 ; year:2018 ; number:4 ; day:07 ; month:08 ; pages:283-292

Links:	Volltext

DOI / URN:	10.1007/s40747-018-0080-1

Katalog-ID:	SPR03721893X

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spelling	10.1007/s40747-018-0080-1 doi (DE-627)SPR03721893X (SPR)s40747-018-0080-1-e DE-627 ger DE-627 rakwb eng Cheng, Ran verfasserin (orcid)0000-0001-9410-8263 aut Model-based evolutionary algorithms: a short survey 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Abstract The evolutionary algorithms (EAs) are a family of nature-inspired algorithms widely used for solving complex optimization problems. Since the operators (e.g. crossover, mutation, selection) in most traditional EAs are developed on the basis of fixed heuristic rules or strategies, they are unable to learn the structures or properties of the problems to be optimized. To equip the EAs with learning abilities, recently, various model-based evolutionary algorithms (MBEAs) have been proposed. This survey briefly reviews some representative MBEAs by considering three different motivations of using models. First, the most commonly seen motivation of using models is to estimate the distribution of the candidate solutions. Second, in evolutionary multi-objective optimization, one motivation of using models is to build the inverse models from the objective space to the decision space. Third, when solving computationally expensive problems, models can be used as surrogates of the fitness functions. Based on the review, some further discussions are also given. Model-based evolutionary algorithms (dpeaa)DE-He213 Estimation of distribution algorithms (dpeaa)DE-He213 Surrogate modelling (dpeaa)DE-He213 Inverse modelling (dpeaa)DE-He213 He, Cheng aut Jin, Yaochu aut Yao, Xin aut Enthalten in Complex & intelligent systems Berlin : SpringerOpen, 2015 4(2018), 4 vom: 07. Aug., Seite 283-292 (DE-627)835589269 (DE-600)2834740-7 2198-6053 nnns volume:4 year:2018 number:4 day:07 month:08 pages:283-292 https://dx.doi.org/10.1007/s40747-018-0080-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2018 4 07 08 283-292
allfields_unstemmed	10.1007/s40747-018-0080-1 doi (DE-627)SPR03721893X (SPR)s40747-018-0080-1-e DE-627 ger DE-627 rakwb eng Cheng, Ran verfasserin (orcid)0000-0001-9410-8263 aut Model-based evolutionary algorithms: a short survey 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Abstract The evolutionary algorithms (EAs) are a family of nature-inspired algorithms widely used for solving complex optimization problems. Since the operators (e.g. crossover, mutation, selection) in most traditional EAs are developed on the basis of fixed heuristic rules or strategies, they are unable to learn the structures or properties of the problems to be optimized. To equip the EAs with learning abilities, recently, various model-based evolutionary algorithms (MBEAs) have been proposed. This survey briefly reviews some representative MBEAs by considering three different motivations of using models. First, the most commonly seen motivation of using models is to estimate the distribution of the candidate solutions. Second, in evolutionary multi-objective optimization, one motivation of using models is to build the inverse models from the objective space to the decision space. Third, when solving computationally expensive problems, models can be used as surrogates of the fitness functions. Based on the review, some further discussions are also given. Model-based evolutionary algorithms (dpeaa)DE-He213 Estimation of distribution algorithms (dpeaa)DE-He213 Surrogate modelling (dpeaa)DE-He213 Inverse modelling (dpeaa)DE-He213 He, Cheng aut Jin, Yaochu aut Yao, Xin aut Enthalten in Complex & intelligent systems Berlin : SpringerOpen, 2015 4(2018), 4 vom: 07. Aug., Seite 283-292 (DE-627)835589269 (DE-600)2834740-7 2198-6053 nnns volume:4 year:2018 number:4 day:07 month:08 pages:283-292 https://dx.doi.org/10.1007/s40747-018-0080-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2018 4 07 08 283-292
allfieldsGer	10.1007/s40747-018-0080-1 doi (DE-627)SPR03721893X (SPR)s40747-018-0080-1-e DE-627 ger DE-627 rakwb eng Cheng, Ran verfasserin (orcid)0000-0001-9410-8263 aut Model-based evolutionary algorithms: a short survey 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Abstract The evolutionary algorithms (EAs) are a family of nature-inspired algorithms widely used for solving complex optimization problems. Since the operators (e.g. crossover, mutation, selection) in most traditional EAs are developed on the basis of fixed heuristic rules or strategies, they are unable to learn the structures or properties of the problems to be optimized. To equip the EAs with learning abilities, recently, various model-based evolutionary algorithms (MBEAs) have been proposed. This survey briefly reviews some representative MBEAs by considering three different motivations of using models. First, the most commonly seen motivation of using models is to estimate the distribution of the candidate solutions. Second, in evolutionary multi-objective optimization, one motivation of using models is to build the inverse models from the objective space to the decision space. Third, when solving computationally expensive problems, models can be used as surrogates of the fitness functions. Based on the review, some further discussions are also given. Model-based evolutionary algorithms (dpeaa)DE-He213 Estimation of distribution algorithms (dpeaa)DE-He213 Surrogate modelling (dpeaa)DE-He213 Inverse modelling (dpeaa)DE-He213 He, Cheng aut Jin, Yaochu aut Yao, Xin aut Enthalten in Complex & intelligent systems Berlin : SpringerOpen, 2015 4(2018), 4 vom: 07. Aug., Seite 283-292 (DE-627)835589269 (DE-600)2834740-7 2198-6053 nnns volume:4 year:2018 number:4 day:07 month:08 pages:283-292 https://dx.doi.org/10.1007/s40747-018-0080-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2018 4 07 08 283-292
allfieldsSound	10.1007/s40747-018-0080-1 doi (DE-627)SPR03721893X (SPR)s40747-018-0080-1-e DE-627 ger DE-627 rakwb eng Cheng, Ran verfasserin (orcid)0000-0001-9410-8263 aut Model-based evolutionary algorithms: a short survey 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Abstract The evolutionary algorithms (EAs) are a family of nature-inspired algorithms widely used for solving complex optimization problems. Since the operators (e.g. crossover, mutation, selection) in most traditional EAs are developed on the basis of fixed heuristic rules or strategies, they are unable to learn the structures or properties of the problems to be optimized. To equip the EAs with learning abilities, recently, various model-based evolutionary algorithms (MBEAs) have been proposed. This survey briefly reviews some representative MBEAs by considering three different motivations of using models. First, the most commonly seen motivation of using models is to estimate the distribution of the candidate solutions. Second, in evolutionary multi-objective optimization, one motivation of using models is to build the inverse models from the objective space to the decision space. Third, when solving computationally expensive problems, models can be used as surrogates of the fitness functions. Based on the review, some further discussions are also given. Model-based evolutionary algorithms (dpeaa)DE-He213 Estimation of distribution algorithms (dpeaa)DE-He213 Surrogate modelling (dpeaa)DE-He213 Inverse modelling (dpeaa)DE-He213 He, Cheng aut Jin, Yaochu aut Yao, Xin aut Enthalten in Complex & intelligent systems Berlin : SpringerOpen, 2015 4(2018), 4 vom: 07. Aug., Seite 283-292 (DE-627)835589269 (DE-600)2834740-7 2198-6053 nnns volume:4 year:2018 number:4 day:07 month:08 pages:283-292 https://dx.doi.org/10.1007/s40747-018-0080-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2018 4 07 08 283-292
language	English
source	Enthalten in Complex & intelligent systems 4(2018), 4 vom: 07. Aug., Seite 283-292 volume:4 year:2018 number:4 day:07 month:08 pages:283-292
sourceStr	Enthalten in Complex & intelligent systems 4(2018), 4 vom: 07. Aug., Seite 283-292 volume:4 year:2018 number:4 day:07 month:08 pages:283-292
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Model-based evolutionary algorithms Estimation of distribution algorithms Surrogate modelling Inverse modelling
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container_title	Complex & intelligent systems
authorswithroles_txt_mv	Cheng, Ran @@aut@@ He, Cheng @@aut@@ Jin, Yaochu @@aut@@ Yao, Xin @@aut@@
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author	Cheng, Ran
spellingShingle	Cheng, Ran misc Model-based evolutionary algorithms misc Estimation of distribution algorithms misc Surrogate modelling misc Inverse modelling Model-based evolutionary algorithms: a short survey
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topic_title	Model-based evolutionary algorithms: a short survey Model-based evolutionary algorithms (dpeaa)DE-He213 Estimation of distribution algorithms (dpeaa)DE-He213 Surrogate modelling (dpeaa)DE-He213 Inverse modelling (dpeaa)DE-He213
topic	misc Model-based evolutionary algorithms misc Estimation of distribution algorithms misc Surrogate modelling misc Inverse modelling
topic_unstemmed	misc Model-based evolutionary algorithms misc Estimation of distribution algorithms misc Surrogate modelling misc Inverse modelling
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title	Model-based evolutionary algorithms: a short survey
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title_sort	model-based evolutionary algorithms: a short survey
title_auth	Model-based evolutionary algorithms: a short survey
abstract	Abstract The evolutionary algorithms (EAs) are a family of nature-inspired algorithms widely used for solving complex optimization problems. Since the operators (e.g. crossover, mutation, selection) in most traditional EAs are developed on the basis of fixed heuristic rules or strategies, they are unable to learn the structures or properties of the problems to be optimized. To equip the EAs with learning abilities, recently, various model-based evolutionary algorithms (MBEAs) have been proposed. This survey briefly reviews some representative MBEAs by considering three different motivations of using models. First, the most commonly seen motivation of using models is to estimate the distribution of the candidate solutions. Second, in evolutionary multi-objective optimization, one motivation of using models is to build the inverse models from the objective space to the decision space. Third, when solving computationally expensive problems, models can be used as surrogates of the fitness functions. Based on the review, some further discussions are also given. © The Author(s) 2018
abstractGer	Abstract The evolutionary algorithms (EAs) are a family of nature-inspired algorithms widely used for solving complex optimization problems. Since the operators (e.g. crossover, mutation, selection) in most traditional EAs are developed on the basis of fixed heuristic rules or strategies, they are unable to learn the structures or properties of the problems to be optimized. To equip the EAs with learning abilities, recently, various model-based evolutionary algorithms (MBEAs) have been proposed. This survey briefly reviews some representative MBEAs by considering three different motivations of using models. First, the most commonly seen motivation of using models is to estimate the distribution of the candidate solutions. Second, in evolutionary multi-objective optimization, one motivation of using models is to build the inverse models from the objective space to the decision space. Third, when solving computationally expensive problems, models can be used as surrogates of the fitness functions. Based on the review, some further discussions are also given. © The Author(s) 2018
abstract_unstemmed	Abstract The evolutionary algorithms (EAs) are a family of nature-inspired algorithms widely used for solving complex optimization problems. Since the operators (e.g. crossover, mutation, selection) in most traditional EAs are developed on the basis of fixed heuristic rules or strategies, they are unable to learn the structures or properties of the problems to be optimized. To equip the EAs with learning abilities, recently, various model-based evolutionary algorithms (MBEAs) have been proposed. This survey briefly reviews some representative MBEAs by considering three different motivations of using models. First, the most commonly seen motivation of using models is to estimate the distribution of the candidate solutions. Second, in evolutionary multi-objective optimization, one motivation of using models is to build the inverse models from the objective space to the decision space. Third, when solving computationally expensive problems, models can be used as surrogates of the fitness functions. Based on the review, some further discussions are also given. © The Author(s) 2018
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Model-based evolutionary algorithms: a short survey

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