DocumentCode :
1727526
Title :
Genetic based learning of a civil engineering problem
Author :
Miles, J.C. ; Moore, C.J.
fYear :
1995
Firstpage :
394
Lastpage :
399
Abstract :
The Classifier System (CS) (Goldberg (1989), Wilson (1994)) is a machine learning process: the machine (a computer program) `learns´ about a particular environment and is then capable of making beneficial decisions or predictions concerning that environment (Forsyth, 1989). The learning method within a CS is approached from an Evolutionary Computation angle: a rule base attempting to define an environment, is optimised with a Genetic Algorithm (GA) (Goldberg, 1989) until that environment is deemed to be `learnt´. CSs are of interest to Engineers, particularly those interested in the fields of intelligent real-time monitoring and control, planning, scheduling, signal processing, operations research, failure analysis and forecasting as well as general dynamic system modelling and prediction (Barclay, 1993). This paper is concerned with the latter of these fields. More specifically, it explains the research being undertaken at UWC regarding the Derivation of Reservoir Control Strategies using a form of CS
Keywords :
civil engineering computing; expert systems; genetic algorithms; learning (artificial intelligence); Classifier System; Reservoir Control Strategies; civil engineering; dynamic system modelling; genetic based learning; rule base;
fLanguage :
English
Publisher :
iet
Conference_Titel :
Genetic Algorithms in Engineering Systems: Innovations and Applications, 1995. GALESIA. First International Conference on (Conf. Publ. No. 414)
Conference_Location :
Sheffield
Print_ISBN :
0-85296-650-4
Type :
conf
DOI :
10.1049/cp:19951081
Filename :
501704
Link To Document :
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