A symbiotic genetic algorithm with local-and-global mapping search for reinforcement fuzzy control

Journal of Intelligent and Fuzzy Systems

Volumn 19, Issue 2, 2008, Pages 103-114

  Juang, Chia Feng ^a  

^a NATIONAL CHUNG HSING UNIVERSITY   (Taiwan)

Author keywords

Coevolutionary computation; Elite strategy; Genetic fuzzy control; Reinforcement learning; Symbiotic evolution

Indexed keywords

CONFORMAL MAPPING; GENETIC ALGORITHMS; REINFORCEMENT LEARNING; SYSTEMS ANALYSIS;

COEVOLUTIONARY COMPUTATION; SYMBIOTIC EVOLUTION;

FUZZY CONTROL;

EID: 42149169124     PISSN: 10641246     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (29)

1. C.T. Lin and C.S.G. Lee, Neural Fuzzy Systems: A Neural-Fuzzy Synergism to Intelligent Systems, Prentice Hall, 1996.
2. L.X. Wang, Adaptive Fuzzy Systems and Control, Englewood Cliffs, NJ: Prentice-Hall, 1994.
3. O. Cordon, F. Herrera, F. Hoffmann and L. Magdalena, Genetic Fuzzy Systems: Evolutionary Tuning and Learning of Fuzzy Knowledge Bases, World Scientific, 2001.
4. D.E. Goldberg, Genetic Algorithms in Search Optimization and Machine Learning, Reading, MA: Addison-Wesley, 1989.
5. C.L. Karr, Design of an adaptive fuzzy logic controller using a genetic algorithm, Proc. the Fourth Int. Conf. Genetic Algorithms, 1991, 450-457.
6. A. Homaifar and E. McCormick, Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms, IEEE Trans Fuzzy Systems 3(2) (1995), 129-139.
7. M. Lee and H. Takagi, Integrating design stages of fuzzy systems using genetic algorithms, Proc. 2nd IEEE Int. Conf. Fuzzy Systems, San Francisco, CA, 1993, 612-617.
8. C.F. Juang, Temporal problems solved by dynamic fuzzy network based on genetic algorithm with variable-length chromosomes, Fuzzy Sets and Systems 142(2) (2004), 199-219.
9. C.F. Juang, A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms, IEEE Trans Fuzzy Systems 10(2) (2002), 155-170.
10. J. Paredis, Coevolutionary computation, Aftifical Life 2(4) (1995), 355-375.
11. M.A. Potter, K.D. Jong and J. Grefenstette, A coevolutionary approach to learning sequential decision rules, Proc. 6th Int. Conf. Genetic Algorithms, Pittsburgh, PA, 1995.
12. M.A. Potter and K.A. DeJong, Cooperative coevolution: An architectural for evolving coadopted subcomponents, Evol Computation 8(1) (2000), 1-29.
13. C.A. Peña-Reyes and M. Sipper, Fuzzy CoCo: a cooperative- coevolutionary approach to fuzzy modeling, IEEE Trans Fuzzy Systems 9(5) (2001), 727-737.
14. C.F. Juang, J.Y. Lin and C.T. Lin, Genetic reinforcement learning through symbiotic evolution for fuzzy controller design, IEEE Trans Systems, Man, and Cyber - Part B: Cybernetics 30(2) (2000), 290-302.
15. D.E. Moriarty and R. Miikkulainen, Efficient reinforcement learning through symbiotic evolution, Machine Learning 22 (1996), 11-32.
16. D.E. Moriarity and R. Miikkulainen, Hierarchical evolution of neural networks, Proc. IEEE Conf. Evolutionary Computation, Anchorage, AK, USA, 1998, 428-433.
17. J.H. Holland, Properties of the bucket bridge brigade algorithm, Proc. First Int. Conf. on Genetic Algorithms and Their Applications, Pittsburge, USA, 1985, 1-7.
18. L.B. Booker, D.E. Goldberg and J.H. Holland, Classifier systems and genetic algorithms, Artificial Intelligence 40 (1989), 235-282.
19. J.H. Holland and J.S. Reitman, Cognitive systems based on adaptive algorithms, in: Pattern-Directed Inference Systems, D.A. Waterman and F. Hayes-Roth, eds, Academic Press, 1978.
20. J.J. Grefenstette, Credit assignment in rule discovery systems based on genetic algorithms, Machine Learning 8(1988), 225-246.
21. H. Ishibuchi, T. Nakashima and T. Murata, Performance evaluation of fuzzy classfier systems for multidimentioinal pattern classification problems, IEEE Trans On Systems, Man and Cybernetics - Part B: Cybernetics 29 (1999), 601-618.
22. R.H. Cannon, Jr., Dynamics of Physical Systems, New York: McGraw-Hill, 1967.
23. K.C. Cheok and N.K. Loh, A ball-balancing demonstration of optimal and disturbance-accommodating control, IEEE Contr Syst Mag (1987), 54-57.
24. A.G. Barto, R.S. Sutton and C.W. Anderson, Neuronlike adaptive elements that can solve difficult learning control problems, IEEE Trans Syst, Man, Cyber 13 (1983), 834-846.
25. C.W. Anderson, Learning to control an inverted pendulum using neural, networks, IEEE Control Systems Magazine 9 (1989), 31-37.
26. D. Whitley, S. Dominic, R. Das and C.W. Anderson, Genetic reinforcement learning for neurocontrol problems, Machine Learning 13 (1993), 259-284.
27. D.B. Fogel, Evolutionary Computation: Towards a New Philosophy of Machine Intelligence, New York, NY: IEEE Press, 1995.
28. T. Bäck and H.P. Schwefel, An overview of evolutionary algorithms for parameter optimization, Evolutionary Computation 1(1) (1993), 1-23.
29. J. Hauser, S. Saatry and P. Kokotovic, Nonlinear control via approximate input-output linearization: the ball and beam example, IEEE Trans on Automatic Control 37(3) (1992), 392-398.