Abstract
This paper presents a new approach to genetic-based machine learning (GBML). The new approach utilizes mechanisms of genetic recombination in bacterial genetics, and the authors have called the new approach “Nagoya approach”. The Nagoya approach is efficient in improving local portions of chromosomes. An obstacle avoidance problem for a mobile robot is simulated using the Nagoya approach, and complex fuzzy rules are found.
This is a preview of subscription content, log in via an institution.
Preview
Unable to display preview. Download preview PDF.
References
D.E.Goldberg, “Genetic Algorithm in Search, Optimization and Machine Learning”, Addison Wesley (1989)
C. L. Karr, L. Freeman, D. Meredith, “Improved Fuzzy Process Control of Spacecraft Autonomous Rendezvous Using a Genetic Algorithm”, SPIE Conf. on Intelligent Control and Adaptive Systems, pp.274–283, 1989
C. L. Karr, “Design of an Adaptive Fuzzy Logic Controller Using a Genetic Algorithm”, Proc. of the 4th Int'l Conf on Genetic Algorithms, pp.450–457, 1991
M. Valenzuela-Rendon, “The Fuzzy Classifier System: A Classifier System for Continuously Varying Variables”, Proc. of the 4th Int'l Conf on Genetic Algorithms, pp.346–353, 1991
T. Furuhashi, K. Nakaoka, K. Morikawa, Y. Uchikawa, “Controlling Excessive Fuzziness in a Fuzzy Classifier System”, Proc. of the 5th Int'l Conf on Genetic Algorithms, p.635, 1993
T. Furuhashi, K. Nakaoka, K. Morikawa, Y. Uchikawa, “An Acquisition of Control Knowledge Using Multiple Fuzzy Classifier Systems”, Journal of Japan Society for Fuzzy Theory and Systems, Vol.6, No.3, pp.603–609, 1994
K. Nakaoka, T. Furuhashi, Y. Uchikawa, “A Study on Apportionment of Credits of Fuzzy Classifier Systems for Knowledge Acquisition of Large Scale Systems”, Proc. of the 3rd Int'l Conf. on Fuzzy Systems, pp.1797–1800, 1994
J. H. Holland, J. S. Reitman, ”Cognitive Systems Based on Adaptive Algorithms”, in Pattern Directed Inference Systems, D. A. Waterman, F. Hayes-Roth (Eds.), pp.313–329. Academic Press, New York, 1978
M. A. Lee, H. Takagi, “Dynamic Control of Genetic Algorithms Using Fuzzy Logic Techniques”, Proc. of the 5th Int'l Conf on Genetic Algorithms, p.76–83, 1993
S. F. Smith,”A Learning System Based on Genetic Adaptive Algorithms”, Ph. D. Thesis, University of Pittsburgh, 1980
J. J. Grefenstette, “Multilevel Credit Assignment in a Genetic Learning System”, Proc. of the 2nd Int'l Conf. on Genetic Algorithms, pp.202–207, 1987
R. Schleif, “Genetics and Molecular Biology (2nd Ed.)”, The Johns Hopkins Univ. Press, 1993
L. Margulis, D. Sagan, “Microcosmos — Four Billion Years of Microbial Evolution”, Summit Books, 1986
J. D. Schaffer, A. Morishima, “An Adaptive Crossover Distribution Mechanism for Genetic Algorithms”, Proc. of the 2nd Int'l Conf. on Genetic Algorithms, pp.36–40, 1987
Y. Davidor, “A Genetic Algorithm Applied to Robot Trajectory Generation”, in Handbook of Genetic Algorithm, L.Davis, Ed., Van Nostrand Reinhold, ch.12, 1991
J. J. Grefenstette, “Lamarckian Learning in Multi-agent Environments”, Proc. of the 4th Int'l Conf. on Genetic Algorithms, pp.303–310, 1991
D.E.Goldberg, “Genetic Algorithm in Search, Optimization and Machine Learning”, pp.202–204 in Chapter 5, Addison Wesley (1989)
H. Mühlenbein, “Parallel Genetic algorithms, Population Genetics and Combinatorial Optimization”, Proc. of the 3rd Int'l Conf. on Genetic Algorithms, pp.416–421, 1989
P. Jog, J. Y. Suh, D. V. Gucht, “The Effects of Population Size, Heuristic Crossover and Local Improvement on a Genetic Algorithm for the Traveling Salesman problem”, Proc. of the 3rd Int'l Conf. on Genetic Algorithm, pp.110–115, 1989
J. A. Miller, W. D. Potter, R.V. Gandham, and C. N. Lapena, “An Evaluation of Local Improvement Operators for Genetic Algorithms”, IEEE Trans. on Systems, Man, and Cybernetics, Vol.23, No.5, 1993
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1995 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Furuhashi, T., Miyata, Y., Nakaoka, K., Uchikawa, Y. (1995). A new approach to genetic based machine learning and an efficient finding of fuzzy rules. In: Furuhashi, T. (eds) Advances in Fuzzy Logic, Neural Networks and Genetic Algorithms. WWW 1994. Lecture Notes in Computer Science, vol 1011. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60607-6_12
Download citation
DOI: https://doi.org/10.1007/3-540-60607-6_12
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-60607-9
Online ISBN: 978-3-540-48457-8
eBook Packages: Springer Book Archive