Designing multilayer perceptrons using a Guided Saw-tooth Evolutionary Programming Algorithm

作     者：Antonio Gutierrez, Pedro Hervas, Cesar Lozano, Manuel 

作者机构：Univ Cordoba Dept Numer Anal & Comp Sci E-14071 Cordoba Spain Univ Granada E-18071 Granada Spain 

出 版 物：《SOFT COMPUTING》 (Soft Comput.)

年 卷 期：2010年第14卷第6期

页      面：599-613页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Spanish Inter-Ministerial Commission of Science and Technology (MICYT) [TIN 2008-06681-C06-03] FEDER "Junta de Andaluca" (Spain) [P08-TIC-3745] Spanish Ministry of Education and Science [AP2006-01746] 

主　　题：Artificial neural networks Multilayer perceptrons Evolutionary algorithms Evolutionary programming Population reinitializations Saw-tooth algorithm 

摘      要：In this paper, a diversity generating mechanism is proposed for an Evolutionary Programming (EP) algorithm that determines the basic structure of Multilayer Perceptron classifiers and simultaneously estimates the coefficients of the models. We apply a modified version of a saw-tooth diversity enhancement mechanism recently presented for Genetic Algorithms, which uses a variable population size and periodic partial reinitializations of the population in the form of a saw-tooth function. Our improvement on this standard scheme consists of guiding saw-tooth reinitializations by considering the variance of the best individuals in the population. The population restarts are performed when the difference of variance between two consecutive generations is lower than a percentage of the previous variance. From the analysis of the results over ten benchmark datasets, it can be concluded that the computational cost of the EP algorithm with a constant population size is reduced by using the original saw-tooth scheme. Moreover, the guided saw-tooth mechanism involves a significantly lower computer time demand than the original scheme. Finally, both saw-tooth schemes do not involve an accuracy decrease and, in general, they obtain a better or similar precision.