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Article: Two-stepped evolutionary algorithm and its application to stability analysis of slopes

TitleTwo-stepped evolutionary algorithm and its application to stability analysis of slopes
Authors
KeywordsAlgorithms
Slope Stability
Stability Analysis
Issue Date2004
Citation
Journal Of Computing In Civil Engineering, 2004, v. 18 n. 2, p. 145-153 How to Cite?
AbstractBased on genetic algorithm and genetic programming, a new evolutionary algorithm is developed to evolve mathematical models for predicting the behavior of complex systems. The input variables of the models are the property parameters of the systems, which include the geometry, the deformation, the strength parameters, etc. On the other hand, the output variables are the system responses, such as displacement, stress, factor of safety, etc. To improve the efficiency of the evolution process, a two-stepped approach is adopted; the two steps are the structure evolution and parameter optimization steps. In the structure evolution step, a family of model structures is generated by genetic programming. Each model structure is a polynomial function of the input variables. An interpreter is then used to construct the mathematical expression for the model through simplification, regularization, and rationalization. Furthermore, necessary internal model parameters are added to the model structures automatically. For each model structure, a genetic algorithm is then used to search for the best values of the internal model parameters in the parameter optimization step. The two steps are repeated until the best model is evolved. The slope stability problem is used to demonstrate that the present method can efficiently generate mathematical models for predicting the behavior of complex engineering systems. ©ASCE.
Persistent Identifierhttp://hdl.handle.net/10722/150285
ISSN
2015 Impact Factor: 1.855
2015 SCImago Journal Rankings: 1.219
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorYang, CXen_US
dc.contributor.authorTham, LGen_US
dc.contributor.authorFeng, XTen_US
dc.contributor.authorWang, YJen_US
dc.contributor.authorLee, PKKen_US
dc.date.accessioned2012-06-26T06:03:01Z-
dc.date.available2012-06-26T06:03:01Z-
dc.date.issued2004en_US
dc.identifier.citationJournal Of Computing In Civil Engineering, 2004, v. 18 n. 2, p. 145-153en_US
dc.identifier.issn0887-3801en_US
dc.identifier.urihttp://hdl.handle.net/10722/150285-
dc.description.abstractBased on genetic algorithm and genetic programming, a new evolutionary algorithm is developed to evolve mathematical models for predicting the behavior of complex systems. The input variables of the models are the property parameters of the systems, which include the geometry, the deformation, the strength parameters, etc. On the other hand, the output variables are the system responses, such as displacement, stress, factor of safety, etc. To improve the efficiency of the evolution process, a two-stepped approach is adopted; the two steps are the structure evolution and parameter optimization steps. In the structure evolution step, a family of model structures is generated by genetic programming. Each model structure is a polynomial function of the input variables. An interpreter is then used to construct the mathematical expression for the model through simplification, regularization, and rationalization. Furthermore, necessary internal model parameters are added to the model structures automatically. For each model structure, a genetic algorithm is then used to search for the best values of the internal model parameters in the parameter optimization step. The two steps are repeated until the best model is evolved. The slope stability problem is used to demonstrate that the present method can efficiently generate mathematical models for predicting the behavior of complex engineering systems. ©ASCE.en_US
dc.languageengen_US
dc.relation.ispartofJournal of Computing in Civil Engineeringen_US
dc.subjectAlgorithmsen_US
dc.subjectSlope Stabilityen_US
dc.subjectStability Analysisen_US
dc.titleTwo-stepped evolutionary algorithm and its application to stability analysis of slopesen_US
dc.typeArticleen_US
dc.identifier.emailTham, LG: hrectlg@hkucc.hku.hken_US
dc.identifier.emailWang, Y: yhwang0062@163.comen_US
dc.identifier.emailLee, PKK: hreclkk@hkucc.hku.hk-
dc.identifier.authorityTham, LG=rp00176en_US
dc.identifier.authorityLee, PKK=rp00141en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1061/(ASCE)0887-3801(2004)18:2(145)en_US
dc.identifier.scopuseid_2-s2.0-16644389528en_US
dc.identifier.hkuros93034-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-16644389528&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume18en_US
dc.identifier.issue2en_US
dc.identifier.spage145en_US
dc.identifier.epage153en_US
dc.identifier.isiWOS:000220572400007-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridYang, CX=7407027740en_US
dc.identifier.scopusauthoridTham, LG=7006213628en_US
dc.identifier.scopusauthoridFeng, XT=7403047624en_US
dc.identifier.scopusauthoridWang, YJ=8265923200en_US
dc.identifier.scopusauthoridLee, PKK=24522826500en_US

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