Incorporating domain-specific knowledge into a genetic algorithm to implement case-based reasoning adaptation
In case-based reasoning systems the adaptation phase is a notoriously difficult and complex step. The design and implementation of an effective case adaptation algorithm is generally determined by the type of application which decides the nature and the structure of the knowledge to be implemented w...
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| Vydáno v: | Knowledge-based systems Ročník 19; číslo 3; s. 192 - 201 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.07.2006
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| ISSN: | 0950-7051, 1872-7409 |
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| Abstract | In case-based reasoning systems the adaptation phase is a notoriously difficult and complex step. The design and implementation of an effective case adaptation algorithm is generally determined by the type of application which decides the nature and the structure of the knowledge to be implemented within the adaptation module, and the level of user involvement during this phase. A new adaptation approach is presented in this paper which uses a modified genetic algorithm incorporating specific domain knowledge and information provided by the retrieved cases. The approach has been developed for a CBR system (CBEM) supporting the use and design of numerical models for estuaries. The adaptation module finds the values of hundreds of parameters for a selected numerical model retrieved from the case-base that is to be used in a new problem context. Without the need of implementing very specific adaptation rules, the proposed approach resolves the problem of acquiring adaptation knowledge by combining the search power of a genetic algorithm with the guidance provided by domain-specific knowledge. The genetic algorithm consists of a modifying version of the classical genetic operations of initialisation, selection, crossover and mutation designed to incorporate practical but general principles of model calibration without reference to any specific problems. The genetic algorithm focuses the search within the parameters' space on those zones that most likely contain the required solutions thus reducing computational time. In addition, the design of the genetic algorithm-based adaptation routine ensures that the parameter values found are suitable for the model approximation and hypotheses, and complies with the problem domain features providing correct and realistic model outputs. This adaptation method is suitable for case-based reasoning systems dealing with numerical modelling applications that require the substitution of a large number of parameter values. |
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| AbstractList | Case-based reasoning systems the adaptation phase is a notoriously difficult and complex step. The design and implementation of an effective case adaptation algorithm is generally determined by the type of application which decides the nature and the structure of the knowledge to be implemented within the adaptation module, and the level of user involvement during this phase. A new adaptation approach is presented in this paper which uses a modified genetic algorithm incorporating specific domain knowledge and information provided by the retrieved cases. The approach has been developed for a CBR system (CBEM) supporting the use and design of numerical models for estuaries. The adaptation module finds the values of hundreds of parameters for a selected numerical model retrieved from the case-base that is to be used in a new problem context. Without the need of implementing very specific adaptation rules, the proposed approach resolves the problem of acquiring adaptation knowledge by combining the search power of a genetic algorithm with the guidance provided by domain-specific knowledge. The genetic algorithm consists of a modifying version of the classical genetic operations of initialisation, selection, crossover and mutation designed to incorporate practical but general principles of model calibration without reference to any specific problems. The genetic algorithm focuses the search within the parameters' space on those zones that most likely contain the required solutions thus reducing computational time. In addition, the design of the genetic algorithm-based adaptation routine ensures that the parameter values found are suitable for the model approximation and hypotheses, and complies with the problem domain features providing correct and realistic model outputs. This adaptation method is suitable for case-based reasoning systems dealing with numerical modelling applications that require the substitution of a large number of parameter values. (Author abstract) In case-based reasoning systems the adaptation phase is a notoriously difficult and complex step. The design and implementation of an effective case adaptation algorithm is generally determined by the type of application which decides the nature and the structure of the knowledge to be implemented within the adaptation module, and the level of user involvement during this phase. A new adaptation approach is presented in this paper which uses a modified genetic algorithm incorporating specific domain knowledge and information provided by the retrieved cases. The approach has been developed for a CBR system (CBEM) supporting the use and design of numerical models for estuaries. The adaptation module finds the values of hundreds of parameters for a selected numerical model retrieved from the case-base that is to be used in a new problem context. Without the need of implementing very specific adaptation rules, the proposed approach resolves the problem of acquiring adaptation knowledge by combining the search power of a genetic algorithm with the guidance provided by domain-specific knowledge. The genetic algorithm consists of a modifying version of the classical genetic operations of initialisation, selection, crossover and mutation designed to incorporate practical but general principles of model calibration without reference to any specific problems. The genetic algorithm focuses the search within the parameters' space on those zones that most likely contain the required solutions thus reducing computational time. In addition, the design of the genetic algorithm-based adaptation routine ensures that the parameter values found are suitable for the model approximation and hypotheses, and complies with the problem domain features providing correct and realistic model outputs. This adaptation method is suitable for case-based reasoning systems dealing with numerical modelling applications that require the substitution of a large number of parameter values. |
| Author | Passone, S. Chung, P.W.H. Nassehi, V. |
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| Cites_doi | 10.1111/j.1747-6593.1993.tb00805.x 10.1002/1099-1085(20000815/30)14:11/12<2089::AID-HYP56>3.0.CO;2-L |
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| References | McDowell, O'Connor (bib6) 1977 Babovic, Wu, Larsen (bib11) 1994 Louis, Johnson (bib10) 1997 French, Clifford (bib8) 2000; 14 Kolodner (bib2) 1993 Jarmulak, Craw, Rowe (bib4) 2001 Goldberg (bib9) 1989 Hanney, Keane (bib1) 1997 W. Wilke, I. Vollrath, K.D. Althoff, R. Bergmann, A framework for learning adaptation knowledge based on knowledge light approaches, Proceedings of the Fifth German Workshop on Case-Based Reasoning, 1997. Leake, Kinley, Wilson (bib3) 1996 J.H. Bikangaga, Mathematical modelling of the hydrodynamics and transport of soluble reactive pollutants in Narrow Tidal Rivers, PhD Thesis, 1993. Thompson (bib7) 1993; 7 Passone, Chung, Nassehi (bib13) 2002 Bikangaga, Nassehi (bib14) 1995; 29 McDowell (10.1016/j.knosys.2005.07.007_bib6) 1977 10.1016/j.knosys.2005.07.007_bib5 French (10.1016/j.knosys.2005.07.007_bib8) 2000; 14 Leake (10.1016/j.knosys.2005.07.007_bib3) 1996 Passone (10.1016/j.knosys.2005.07.007_bib13) 2002 Kolodner (10.1016/j.knosys.2005.07.007_bib2) 1993 Goldberg (10.1016/j.knosys.2005.07.007_bib9) 1989 Babovic (10.1016/j.knosys.2005.07.007_bib11) 1994 Bikangaga (10.1016/j.knosys.2005.07.007_bib14) 1995; 29 Hanney (10.1016/j.knosys.2005.07.007_bib1) 1997 Thompson (10.1016/j.knosys.2005.07.007_bib7) 1993; 7 10.1016/j.knosys.2005.07.007_bib12 Jarmulak (10.1016/j.knosys.2005.07.007_bib4) 2001 Louis (10.1016/j.knosys.2005.07.007_bib10) 1997 |
| References_xml | – year: 1993 ident: bib2 article-title: Case-Based Reasoning – year: 2002 ident: bib13 article-title: Case-based reasoning for estuarine model design publication-title: Proceedings of the Sixth European Conference on Case-Based Reasoning – year: 1997 ident: bib10 article-title: Solving similar problems using genetic algorithms and case-base memory publication-title: Proceedings of the Seventh International Conference on Genetic Algorithms – volume: 14 start-page: 2089 year: 2000 end-page: 2108 ident: bib8 article-title: Hydrodynamic modelling as a basis for explaining estuarine environmental dynamics: some computational and methological issues publication-title: Hydrological Process – reference: W. Wilke, I. Vollrath, K.D. Althoff, R. Bergmann, A framework for learning adaptation knowledge based on knowledge light approaches, Proceedings of the Fifth German Workshop on Case-Based Reasoning, 1997. – year: 1997 ident: bib1 article-title: The adaptation knowledge: how to easy it by learning from cases publication-title: Proceedings of the Second International Conference on Case-Based Reasoning – year: 1977 ident: bib6 article-title: Hydraulic Behaviour of Estuaries – volume: 29 start-page: 2367 year: 1995 end-page: 2375 ident: bib14 article-title: Application of computer modelling techniques to the determination of optimum effluent discharge policies in Tidal water systems publication-title: Water Resources – year: 1994 ident: bib11 article-title: Calibrating hydrodynamics models by means of simulated evolution publication-title: Proceedings of the First International Conference in Hydroinformatics – year: 1989 ident: bib9 article-title: Genetic Algorithm in Search, Optimisation and Machine Learning – year: 1996 ident: bib3 article-title: Acquiring case adaptation knowledge: a hybrid approach publication-title: Proceedings of the Thirteenth National Conference on Artificial Intelligence – year: 2001 ident: bib4 article-title: Using case-base data to learn adaptation knowledge for design publication-title: Proceedings of the Seventeenth IJCAI Conference – volume: 7 start-page: 18 year: 1993 end-page: 23 ident: bib7 article-title: Mathematical-models and engineering design publication-title: Journal of the Institution of Water and Environmental Management – reference: J.H. Bikangaga, Mathematical modelling of the hydrodynamics and transport of soluble reactive pollutants in Narrow Tidal Rivers, PhD Thesis, 1993. – volume: 29 start-page: 2367 issue: 10 year: 1995 ident: 10.1016/j.knosys.2005.07.007_bib14 article-title: Application of computer modelling techniques to the determination of optimum effluent discharge policies in Tidal water systems publication-title: Water Resources – year: 1996 ident: 10.1016/j.knosys.2005.07.007_bib3 article-title: Acquiring case adaptation knowledge: a hybrid approach – volume: 7 start-page: 18 year: 1993 ident: 10.1016/j.knosys.2005.07.007_bib7 article-title: Mathematical-models and engineering design publication-title: Journal of the Institution of Water and Environmental Management doi: 10.1111/j.1747-6593.1993.tb00805.x – volume: 14 start-page: 2089 year: 2000 ident: 10.1016/j.knosys.2005.07.007_bib8 article-title: Hydrodynamic modelling as a basis for explaining estuarine environmental dynamics: some computational and methological issues publication-title: Hydrological Process doi: 10.1002/1099-1085(20000815/30)14:11/12<2089::AID-HYP56>3.0.CO;2-L – year: 1997 ident: 10.1016/j.knosys.2005.07.007_bib10 article-title: Solving similar problems using genetic algorithms and case-base memory – year: 1993 ident: 10.1016/j.knosys.2005.07.007_bib2 – year: 2001 ident: 10.1016/j.knosys.2005.07.007_bib4 article-title: Using case-base data to learn adaptation knowledge for design – year: 1994 ident: 10.1016/j.knosys.2005.07.007_bib11 article-title: Calibrating hydrodynamics models by means of simulated evolution – ident: 10.1016/j.knosys.2005.07.007_bib5 – year: 1977 ident: 10.1016/j.knosys.2005.07.007_bib6 – year: 2002 ident: 10.1016/j.knosys.2005.07.007_bib13 article-title: Case-based reasoning for estuarine model design – year: 1989 ident: 10.1016/j.knosys.2005.07.007_bib9 – ident: 10.1016/j.knosys.2005.07.007_bib12 – year: 1997 ident: 10.1016/j.knosys.2005.07.007_bib1 article-title: The adaptation knowledge: how to easy it by learning from cases |
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| SubjectTerms | Adaptation knowledge Case adaptation Case based reasoning Case-based reasoning system Computer science Domain knowledge Genetic algorithm |
| Title | Incorporating domain-specific knowledge into a genetic algorithm to implement case-based reasoning adaptation |
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