Interval GA for evolving neural networks with interval weights and biases

Author

Okada, Hidehiko ; Matsuse, Takashi ; Wada, Tetsuya ; Yamashita, Akira

Author_Institution

Grad. Sch. of Frontier Inf., Kyoto Sangyo Univ., Kyoto, Japan

fYear

2012

fDate

20-23 Aug. 2012

Firstpage

1542

Lastpage

1545

Abstract

In this paper, we propose an extension of genetic algorithm for neuroevolution of interval-valued neural networks. In the proposed GA, values in the genotypes are not real numbers but intervals. We apply our interval-valued GA (IvGA) to the approximate modeling of interval functions with interval-valued neural networks. Experimental results showed that INNs trained by our IvGA approximated a test function to a certain extent, despite the fact that the learning was not supervised.

Keywords

approximation theory; evolutionary computation; genetic algorithms; learning (artificial intelligence); neural nets; INN training; IvGA; genetic algorithm; genotypes; interval biases; interval function approximate modeling; interval weights; interval-valued GA; interval-valued neural networks; learning; neuroevolution; test function; Artificial neural networks; Evolutionary computation; Genetic algorithms; Gold; Sociology; Statistics; Evolutionary algorithms; genetic algorithm; interval arithmetic; neural network; neuroevolution;

fLanguage

English

Publisher

ieee

Conference_Titel

SICE Annual Conference (SICE), 2012 Proceedings of

Conference_Location

Akita

ISSN

pending

Print_ISBN

978-1-4673-2259-1

Type

conf

Filename

6318696