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- W2099135983 abstract "Evolutionary Computation is a typical paradigm for the Radial Basis Function Network design. In this environment an individual represents a whole network. An alternative is to use cooperative-competitive methods where an individual is a part of the solution. CO <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> RBFN is an evolutionary cooperative-competitive hybrid methodology for the design of Radial Basis Function Networks. In the proposed cooperative-competitive environment, each individual represents a Radial Basis Function, and the entire population is responsible for the final solution. In order to calculate the application probability of the evolutive operators over a certain Radial Basis Function, a Fuzzy Rule Based System has been used. In this paper, CO <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> RBFN is adapted to the regression problem and an analysis of mutation operator is performed. To do so, two implementation of the mutation operator, based on gradient and based on clustering, have been implemented and tested. The results have been compared with other data mining and mathematical methods usually used in regression problems." @default.
- W2099135983 created "2016-06-24" @default.
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- W2099135983 date "2010-07-01" @default.
- W2099135983 modified "2023-10-17" @default.
- W2099135983 title "A preliminary study on mutation operators in cooperative competitive algorithms for RBFN design" @default.
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- W2099135983 doi "https://doi.org/10.1109/ijcnn.2010.5596330" @default.
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