List Books » Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods
Authors: Nikolay Y. Nikolaev, Hitoshi Iba
ISBN-13: 9780387312392, ISBN-10: 0387312390
Format: Hardcover
Publisher: Springer-Verlag New York, LLC
Date Published: May 2006
Edition: Genetic and Evolutionary Computation
Adaptive Learning of Polynomial Networks delivers theoretical and practical knowledge for the development of algorithms that infer linear and non-linear multivariate models, providing a methodology for inductive learning of polynomial neural network models (PNN) from data. The empirical investigations detailed here demonstrate that PNN models evolved by genetic programming and improved by backpropagation are successful when solving real-world tasks.
The text emphasizes the model identification process and presents
This volume is an essential reference for researchers and practitioners interested in the fields of evolutionary computation, artificial neural networks and Bayesian inference, and will also appeal to postgraduate and advanced undergraduate students of genetic programming. Readers will strengthen their skills in creating both efficient model representations and learning operators that efficiently sample the search space, navigating the search process through the design of objective fitness functions, and examining the search performance of the evolutionary system.
1 | Introduction | 1 |
2 | Inductive genetic programming | 25 |
3 | Tree-like PNN representations | 55 |
4 | Fitness functions and landscapes | 81 |
5 | Search navigation | 111 |
6 | Backpropagation techniques | 147 |
7 | Temporal backpropagation | 181 |
8 | Bayesian inference techniques | 209 |
9 | Statistical model diagnostics | 241 |
10 | Time series modelling | 273 |
11 | Conclusions | 291 |