Book Chapter10.1007/3-540-45720-8_64
Expert Mutation Operators for the Evolution of Radial Basis Function Neural Networks
Jesús González,Ignacio Rojas,Héctor Pomares,Moisés Salmerón +3 more
- 13 Jun 2001
- pp 538-545
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TL;DR: It is shown that the expertknowledge is not always able to improve the results obtained by a blind evolutionary algorithm, and that the final results depend strongly on how the expert knowledge is utilized.
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Abstract: This paper compares some mutationoperators containing expert knowledge about the problem of optimizing the parameters of a Radial Basis Function Neural Network. It is shown that the expert knowledge is not always able to improve the results obtained by a blind evolutionary algorithm, and that the final results depend strongly on how the expert knowledge is utilized.
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Citations
Evolutionary optimization of radial basis function classifiers for data mining applications
Oliver Buchtala,Manuel Klimek,Bernhard Sick +2 more
- 01 Oct 2005
TL;DR: An evolutionary algorithm (EA) that performs feature and model selection simultaneously for radial basis function (RBF) classifiers is described that is independent of specific applications so that many ideas and solutions can be transferred to other classifier paradigms.
Evolving RBF neural networks for time-series forecasting with EvRBF
TL;DR: In this work, evolutionary algorithms are used to automatically build a radial basis function neural networks (RBF NN) that solves a specified problem, in this case related to currency exchange rates forecasting.
114
Connectionist models of neurons, learning processes, and artificial intelligence
José Mira,Alberto Prieto +1 more
- 01 Jan 2001
TL;DR: Learning and Other Plasticity Phenomena, and Complex Systems Dynamics.
88
Evolved RBF Networks for Time-Series Forecasting and Function Approximation
Víctor Manuel Rivas Sanchos,Pedro Ángel Castillo Valdivieso,Juan Julián Merelo Guervós +2 more
- 07 Sep 2002
TL;DR: An evolutionary algorithm with specific operators has been developed to automatically find Radial basis Functions Neural Networks that solve a given problem.
3
Designing a phenotypic distance index for radial basis function neural networks
Jesús González,Ignacio Rojas,Héctor Pomares,Julio Ortega +3 more
- 03 Jun 2003
TL;DR: It is shown that AC-SAT outperforms the other evolutionary meta-heuristics especially the scatter search, which has been developed recently.
2
References
•Book
Adaptation in natural and artificial systems
John H. Holland
- 01 Jan 1975
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
•Book
Genetic Algorithms + Data Structures = Evolution Programs
Zbigniew Michalewicz
- 01 Jan 1992
TL;DR: GAs and Evolution Programs for Various Discrete Problems, a Hierarchy of Evolution Programs and Heuristics, and Conclusions.
13.5K
Orthogonal least squares learning algorithm for radial basis function networks
TL;DR: The authors propose an alternative learning procedure based on the orthogonal least-squares method, which provides a simple and efficient means for fitting radial basis function networks.
3.5K
Solution of linear equations by diagonalization of coefficients matrix
TL;DR: In this paper, the authors proposed an infinite convergent product of simple unitary matrices un{zn) which represent plane rotations through complex angles z = 9n + in.
Analysis of the Functional Block Involved in the Design of Radial Basis Function Networks
Ignacio Rojas,Héctor Pomares,J. Gonzales,José Luis Bernier,Eduardo Ros,Francisco J. Pelayo,Alberto Prieto +6 more
TL;DR: In the present contribution, the relevance and relative importance of the parameters involved in such a design are investigated by using a statistical tool, the ANalysis of the VAriance (ANOVA), and various problems of classification, functional approximation and time series estimation are analyzed.
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