Genetic algorithm applied to maze passing problem of mobile robot-a comparison with the learning performance of the hierarchical structure stochastic automata

Author

Baba, Norio ; Hanada, Hirotoshi

Author_Institution

Osaka Kyoiku Univ., Kashihara, Japan

Volume

4

fYear

1994

fDate

27 Jun-2 Jul 1994

Firstpage

2690

Abstract

The study of genetic algorithms has been done quite extensively by many researchers. It has now reached certain level of maturity. However, only few of the comparisons with other optimization methods have been reported so far. In this paper, the performance of the genetic algorithms is compared with that of the hierarchical structure stochastic automata by utilizing an example of maze passing problem of mobile robots. It appears that several computer simulation results contained in this paper may be helpful for the discussion concerning the efficiency of the genetic algorithms

Keywords

genetic algorithms; learning (artificial intelligence); mobile robots; neural nets; path planning; stochastic automata; genetic algorithms; hierarchical structure stochastic automata; learning process; maze passing problem; mobile robot; optimization; path planning; Cities and towns; Computer simulation; Genetic algorithms; Information science; Learning automata; Learning systems; Mobile robots; Probability distribution; Robotics and automation; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374647

Filename

374647