An Investigation on Noisy Environments in Evolutionary Multiobjective Optimization

In addition to satisfying several competing objectives, many real-world applications are also characterized by a certain degree of noise, manifesting itself in the form of signal distortion or uncertain information. In this paper, extensive studies are carried out to examine the impact of noisy envi...

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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 11; H. 3; S. 354 - 381
Hauptverfasser:	Goh, C.K., Tan, K.C.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.06.2007 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Applied sciences Archiving Artificial intelligence Computer science; control theory; systems Constraint optimization Convergence Distortion Evolutionary Evolutionary algorithms Evolutionary algorithms (EAs) Evolutionary computation Exact sciences and technology Learning and adaptive systems multiobjective optimization Noise measurement Noise robustness noisy fitness function Optimization Parallel processing Search methods Searching Signal distortion Stochastic processes Strategy Studies Working environment noise Discontinuity Local search Probabilistic approach Evolutionary algorithm Empirical method Multiobjective programming Optimization multiobjective optimization Environment impact Imperfect information Evolutionary algorithms (EAs) Robustness noisy fitness function Signal distortion Signal to noise ratio
ISSN:	1089-778X, 1941-0026
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Abstract	In addition to satisfying several competing objectives, many real-world applications are also characterized by a certain degree of noise, manifesting itself in the form of signal distortion or uncertain information. In this paper, extensive studies are carried out to examine the impact of noisy environments in evolutionary multiobjective optimization. Three noise-handling features are then proposed based upon the analysis of empirical results, including an experiential learning directed perturbation operator that adapts the magnitude and direction of variation according to past experiences for fast convergence, a gene adaptation selection strategy that helps the evolutionary search in escaping from local optima or premature convergence, and a possibilistic archiving model based on the concept of possibility and necessity measures to deal with problem of uncertainties. In addition, the performances of various multiobjective evolutionary algorithms in noisy environments, as well as the robustness and effectiveness of the proposed features are examined based upon five benchmark problems characterized by different difficulties in local optimality, nonuniformity, discontinuity, and nonconvexity
AbstractList	Three noise-handling features are then proposed based upon the analysis of empirical results, including an experiential learning directed perturbation operator that adapts the magnitude and direction of variation according to past experiences for fast convergence, a gene adaptation selection strategy that helps the evolutionary search in escaping from local optima or premature convergence, and a possibilistic archiving model based on the concept of possibility and necessity measures to deal with problem of uncertainties. In addition to satisfying several competing objectives, many real-world applications are also characterized by a certain degree of noise, manifesting itself in the form of signal distortion or uncertain information. In this paper, extensive studies are carried out to examine the impact of noisy environments in evolutionary multiobjective optimization. Three noise-handling features are then proposed based upon the analysis of empirical results, including an experiential learning directed perturbation operator that adapts the magnitude and direction of variation according to past experiences for fast convergence, a gene adaptation selection strategy that helps the evolutionary search in escaping from local optima or premature convergence, and a possibilistic archiving model based on the concept of possibility and necessity measures to deal with problem of uncertainties. In addition, the performances of various multiobjective evolutionary algorithms in noisy environments, as well as the robustness and effectiveness of the proposed features are examined based upon five benchmark problems characterized by different difficulties in local optimality, nonuniformity, discontinuity, and nonconvexity In addition to satisfying several competing objectives, many real-world applications are also characterized by a certain degree of noise, manifesting itself in the form of signal distortion or uncertain information. In this paper, extensive studies are carried out to examine the impact of noisy environments in evolutionary multiobjective optimization. Three noise-handling features are then proposed based upon the analysis of empirical results, including an experiential learning directed perturbation operator that adapts the magnitude and direction of variation according to past experiences for fast convergence, a gene adaptation selection strategy that helps the evolutionary search in escaping from local optima or premature convergence, and a possibilistic archiving model based on the concept of possibility and necessity measures to deal with problem of uncertainties. In addition, the performances of various multiobjective evolutionary algorithms in noisy environments, as well as the robustness and effectiveness of the proposed features are examined based upon five benchmark problems characterized by different difficulties in local optimality, nonuniformity, discontinuity, and nonconvexity.
Author	Tan, K.C. Goh, C.K.
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Cites_doi	10.1002/j.1538-7305.1948.tb01338.x 10.1162/106365604773955139 10.1016/S0933-3657(02)00014-3 10.1162/evco.1999.7.3.205 10.1109/TEVC.2003.810760 10.1016/j.ejor.2004.08.038 10.1109/CEC.2002.1007013 10.1109/CEC.2003.1299404 10.1109/TSMCB.2002.804372 10.1016/S0045-7825(99)00386-2 10.1613/jair.842 10.1109/4235.974840 10.1007/3-540-44719-9_23 10.1007/978-1-4684-5287-7 10.1016/S0377-2217(98)00262-8 10.1109/CEC.2000.870296 10.1109/TEVC.2003.812220 10.1017/S0890060401020054 10.1109/CEC.2000.870274 10.1109/TEVC.2003.810758 10.1162/106365600568202 10.1109/TSMCA.2004.824873 10.1109/TEVC.2003.810761 10.1162/evco.1994.2.3.221 10.1109/CEC.2002.1006261 10.1109/4235.910466 10.1214/aoms/1177704472 10.1162/106365600568167 10.1109/4235.797969 10.1007/BF03325101 10.1109/4235.996017 10.1109/ICEC.1994.350045 10.1109/TAES.2004.1310019 10.1109/CEC.2000.870756 10.1109/CEC.2002.1007024 10.1109/4235.735433 10.1109/4235.985690 10.1109/TEVC.2003.810759 10.1007/3-540-44719-9_22 10.1145/298151.298382 10.1023/A:1015516501242 10.1109/CEC.2001.934294 10.1016/S0165-0114(01)00161-0 10.1007/s10462-004-2902-3
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References	ref57 ref13 ref15 ref14 ref53 ref52 ref55 ref54 ref17 ref16 branke (ref6) 2001 rana (ref33) 1996 zitzler (ref56) 2001 giraud-carrier (ref8) 2000 ref51 ref50 singh (ref41) 2003 ref46 ref45 ref48 ref42 ref44 goldberg (ref19) 1987 ref43 ref49 coello (ref10) 2002; 16 ref7 ref4 ref3 ref5 ref40 fonseca (ref18) 1993 ref35 goldberg (ref20) 2002 scott (ref39) 1995 ref37 rudolph (ref36) 2001; 1 ref31 ref30 coello (ref9) 1999; 1 rattray (ref34) 1997 ref32 deb (ref12) 1989 ref2 ref1 ref38 tan (ref47) 2003; 18 ref24 corne (ref11) 2000 ref26 ref25 ref22 ref21 ref28 ref27 ref29 hughes (ref23) 2001; 2
References_xml	– ident: ref40 doi: 10.1002/j.1538-7305.1948.tb01338.x – ident: ref15 doi: 10.1162/106365604773955139 – volume: 1 start-page: 318 year: 2001 ident: ref36 article-title: a partial order approach to noisy fitness functions publication-title: Proc Congr Evol Comput – ident: ref46 doi: 10.1016/S0933-3657(02)00014-3 – ident: ref13 doi: 10.1162/evco.1999.7.3.205 – year: 2002 ident: ref20 publication-title: The Design of Innovation Lessons from and for Competent Genetic Algorithms – ident: ref53 doi: 10.1109/TEVC.2003.810760 – ident: ref48 doi: 10.1016/j.ejor.2004.08.038 – ident: ref27 doi: 10.1109/CEC.2002.1007013 – ident: ref49 doi: 10.1109/CEC.2003.1299404 – ident: ref7 doi: 10.1109/TSMCB.2002.804372 – start-page: 41 year: 1987 ident: ref19 article-title: genetic algorithms with sharing for multi-modal function optimization publication-title: Proc 2nd Int Conf Genetic Algorithms – ident: ref4 doi: 10.1016/S0045-7825(99)00386-2 – volume: 18 start-page: 183 year: 2003 ident: ref47 article-title: an evolutionary algorithm with advanced goal and priority specification for multi-objective optimization publication-title: J Artif Intell Res doi: 10.1613/jair.842 – ident: ref44 doi: 10.1109/4235.974840 – ident: ref22 doi: 10.1007/3-540-44719-9_23 – ident: ref16 doi: 10.1007/978-1-4684-5287-7 – ident: ref21 doi: 10.1016/S0377-2217(98)00262-8 – start-page: 839 year: 2000 ident: ref11 article-title: the pareto envelope-based selection algorithm for multiobjective optimization publication-title: Proc 6th Int Conf Parallel Problem Solving From Nature (PPSN VI) – ident: ref52 doi: 10.1109/CEC.2000.870296 – ident: ref30 doi: 10.1109/TEVC.2003.812220 – volume: 16 start-page: 39 year: 2002 ident: ref10 publication-title: Artificial Intelligence in Eng Design Analysis and Manufacturing doi: 10.1017/S0890060401020054 – ident: ref29 doi: 10.1109/CEC.2000.870274 – ident: ref57 doi: 10.1109/TEVC.2003.810758 – start-page: 416 year: 1993 ident: ref18 article-title: genetic algorithm for multiobjective optimization, formulation, discussion and generalization publication-title: Proc 5th Int Conf Genetic Algorithms – year: 1995 ident: ref39 publication-title: Fault tolerant design using single and multi-criteria genetic algorithms – ident: ref55 doi: 10.1162/106365600568202 – ident: ref17 doi: 10.1109/TSMCA.2004.824873 – ident: ref5 doi: 10.1109/TEVC.2003.810761 – start-page: 243 year: 2001 ident: ref6 article-title: efficient fitness estimation in noisy environments publication-title: Proc Genetic Evolutionary Computatation Conf – ident: ref38 doi: 10.1162/evco.1994.2.3.221 – start-page: 198 year: 1996 ident: ref33 article-title: searching in the presence of noise publication-title: Proc 4th Int Conf Parallel Problem Solving from Nature (PPSN IV) – ident: ref37 doi: 10.1109/CEC.2002.1006261 – ident: ref42 doi: 10.1109/4235.910466 – ident: ref32 doi: 10.1214/aoms/1177704472 – ident: ref26 doi: 10.1162/106365600568167 – start-page: 117 year: 1997 ident: ref34 publication-title: Foundations of Genetic Algorithms 4 – ident: ref54 doi: 10.1109/4235.797969 – volume: 1 start-page: 269 year: 1999 ident: ref9 article-title: a comprehensive survey of evolutionary-based multiobjective optimization techniques publication-title: Int J Knowl Inf Syst doi: 10.1007/BF03325101 – start-page: 42 year: 1989 ident: ref12 article-title: an investigation on niche and species formation in genetic function optimization publication-title: Proc 3rd Int Conf Genetic Algorithms – ident: ref14 doi: 10.1109/4235.996017 – ident: ref3 doi: 10.1109/ICEC.1994.350045 – ident: ref28 doi: 10.1109/TAES.2004.1310019 – ident: ref35 doi: 10.1109/CEC.2000.870756 – ident: ref2 doi: 10.1109/CEC.2002.1007024 – ident: ref31 doi: 10.1109/4235.735433 – start-page: 36 year: 2000 ident: ref8 article-title: unifying learning with evolution through baldwinian evolution and lamarckism: a case study publication-title: Proc Symp Comput Intell Learning – ident: ref1 doi: 10.1109/4235.985690 – ident: ref24 doi: 10.1109/TEVC.2003.810759 – ident: ref50 doi: 10.1007/3-540-44719-9_22 – ident: ref51 doi: 10.1145/298151.298382 – ident: ref45 doi: 10.1023/A:1015516501242 – year: 2001 ident: ref56 publication-title: SPEA2 Improving the strength Pareto evolutionary algorithm – volume: 2 start-page: 963 year: 2001 ident: ref23 article-title: constraint handling with uncertain and noisy multi-objective evolution publication-title: Proc 2001 Congr Evol Comput doi: 10.1109/CEC.2001.934294 – ident: ref43 doi: 10.1016/S0165-0114(01)00161-0 – ident: ref25 doi: 10.1007/s10462-004-2902-3 – year: 2003 ident: ref41 publication-title: Uncertainty based multi-objective optimization of groundwater remediation design
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SubjectTerms	Algorithms Applied sciences Archiving Artificial intelligence Computer science; control theory; systems Constraint optimization Convergence Distortion Evolutionary Evolutionary algorithms Evolutionary algorithms (EAs) Evolutionary computation Exact sciences and technology Learning and adaptive systems multiobjective optimization Noise measurement Noise robustness noisy fitness function Optimization Parallel processing Search methods Searching Signal distortion Stochastic processes Strategy Studies Working environment noise
Title	An Investigation on Noisy Environments in Evolutionary Multiobjective Optimization
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