Keywords
Genetic Algorithms, Case-Based Reasoning, Model Interoperability, Intelligent Environmental Decision Support Systems
Location
Session B2: Semantic Interoperability of Models in Intelligent Environmental Decision Support Systems (IEDSS)
Start Date
17-6-2014 3:40 PM
End Date
17-6-2014 5:20 PM
Abstract
Interoperability of Intelligent Environmental Decision Support Systems (IEDSS) is one open challenge in the IEDSS field. This paper shows the interoperability of Evolutionary Computation, concretely Genetic Algorithms (GA), and Case-Based Reasoning (CBR) in IEDSS through the GESCONDA tool. GESCONDA is a tool for the deployment of Intelligent Decision Support Systems. This interoperability has been tested with several domains with different purposes like classification tasks, predictive tasks, etc. In the paper, the application in one environmental domain is described and analysed. The experimentation results indicate that this interoperation of both methods can improve the results of the application of one single method, CBR or GA. Thus, the potential of this kind of interoperation seems to be very good and it is an illustrating example of the benefits of Interoperable Intelligent Environmental Decision Support Systems.
Included in
Civil Engineering Commons, Data Storage Systems Commons, Environmental Engineering Commons, Hydraulic Engineering Commons, Other Civil and Environmental Engineering Commons
Evolutionary Computation and Case-Based Reasoning Interoperation in IEDSS through GESCONDA
Session B2: Semantic Interoperability of Models in Intelligent Environmental Decision Support Systems (IEDSS)
Interoperability of Intelligent Environmental Decision Support Systems (IEDSS) is one open challenge in the IEDSS field. This paper shows the interoperability of Evolutionary Computation, concretely Genetic Algorithms (GA), and Case-Based Reasoning (CBR) in IEDSS through the GESCONDA tool. GESCONDA is a tool for the deployment of Intelligent Decision Support Systems. This interoperability has been tested with several domains with different purposes like classification tasks, predictive tasks, etc. In the paper, the application in one environmental domain is described and analysed. The experimentation results indicate that this interoperation of both methods can improve the results of the application of one single method, CBR or GA. Thus, the potential of this kind of interoperation seems to be very good and it is an illustrating example of the benefits of Interoperable Intelligent Environmental Decision Support Systems.
