DocumentCode :
2730545
Title :
Adaptive cluster covering and evolutionary approach: comparison, differences and similarities
Author :
Solomatine, Dimitri P.
Author_Institution :
UNESCO-IHE Inst. for Water Educ., Delft, Netherlands
Volume :
3
fYear :
2005
fDate :
2-5 Sept. 2005
Firstpage :
1959
Abstract :
In case the objective function to be minimized is not known analytically and no assumption can be made about the single extremum, global optimization (GO) methods must be used. Paper gives a brief overview of GO methods, with the special attention to principles of clustering, covering and evolution. Nine algorithms, including a simple GA, are compared in terms of effectiveness (accuracy), efficiency (number of the needed function evaluations) and reliability on several problems. Particular features of adaptive cluster covering algorithm (ACCO) leading to its high efficiency are analyzed and compared with those of an evolutionary approach. The possibilities of (partially) attributing ACCO and other GO algorithms to the group of EA are considered.
Keywords :
adaptive systems; evolutionary computation; genetic algorithms; hydrology; minimisation; pattern clustering; reliability theory; adaptive cluster covering algorithm; evolutionary algorithms; genetic algorithms; global optimization; reliability theory; Algorithm design and analysis; Calibration; Clustering algorithms; Constraint optimization; Genetic algorithms; Hydrology; Minimization methods; Optimization methods; Parameter estimation; Robustness;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2005. The 2005 IEEE Congress on
Print_ISBN :
0-7803-9363-5
Type :
conf
DOI :
10.1109/CEC.2005.1554935
Filename :
1554935
Link To Document :
بازگشت