EVOLUTIONARY SYSTEM TO MODEL STRUCTURE AND PARAMETERS REGRESSION

• Published in: Neural Network World Vol. 22; no. 2; pp. 181 - 194
• Main Author: Brandejsky, Tomas
• Format: Journal Article
• Language: English
• Published: Prague Institute of Information and Computer Technology 01.01.2012; Institute of Computer Science
• Subjects: Genetic algorithms; Neural networks; Regression analysis; model development; genetic programming algorithm; Genetic algorithm; symbolic regression; evolutionary strategy
• ISSN: 1210-0552; 2336-4335
• DOI: 10.14311/NNW.2012.22.011

This paper discusses features of multilayered evolutionary system suitable to identify various systems including their model symbolic regression. Improved sensitivity allows modeling of difficult systems as deterministic chaos ones. The presented paper starts with a brief introduction to previous works and ideas which allowed to build the presented two abstraction levels system. Then the structure of Genetic Programming Algorithm - Evolutionary Strategy hybrid system is described and analyzed, including such problems as suitability to parallel implementation, optimal set of building blocks, or initial population generating rules. GPA-ES system combines GPA to model development with ES used for model parameter estimation and optimization. Such a hybrid system eliminates many weaknesses of standard GPA. The paper concludes with examples of GPA-ES application to Lorenz and Rösler systems regression and suggests application to Neural Network Model design.