Abstract
In recent years, the new concept of DNA (deoxyribonucleic acid) computing has drawn intensive research interests. The idea of DNA computing, proposed by Leonard Adleman (Leonard 1994) in 1994, is to express a problem in the form of DNA molecules and to realize the computation by operating on those DNA molecules. There are two major advantages of DNA computing: the great parallel computation power and the mega information storage ability. DNA computing is quick, as it can perform many calculations simultaneously or in parallel (Boneh et al 1995; Winfree 1995). Some of the very complex problems which are hard even for supercomputers can be solved by DNA computing (Lipton 1995; Boneh et al 1995). DNA computing also provides a huge storage media since it stores the information in DNA molecules (Baum 1995). DNA computing is such a novel idea that its future applications still remain unknown. However, it seems that DNA computing will make great changes in the fields of computer science, biology, chemistry and medicine (Leonard 1996; Beaver 1995).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Baum, E. (1995) Building an Associative Memory Vastly Larger Than the Brain. Science, 268: 583–585.
Beaver, D. (1995) A universal molecular computer. Technical Report CSE-95–01, Penn State University.
Boneh, D., Dunworth, C., Lipton, R.J. and Sgall, J. (1995) On the computational power of DNA. Technical Report CS-TR-499–95, Princeton University.
Boneh, D., Dunworth, C. and Lipton, R.J. (1995) Breaking DES using a molecular computer. Technical Report CS-TR-489–95, Princeton University.
Cyberbotics. (1999) Webots 2.0 User Guide.
Cyberbotics. (1999) Webots 2.0 Reference Manaul.
Furuhashi, T. (1997) Development of IFOHEN rules with the use of dna coding. in: W. Pedrycz (Ed.), Fuzzy Evolutionary Computation, Kluwer Academic Publishers, Boston.
Karr, C.L. (1997) Fuzzy-evolutionary Systems. In T. Back, D.B.Fogel, and Z.Michalewicz (eds) Handbook of Evolutionary Computation, Oxford University Press.
Holland, J. (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press.
Homaifar, A. and McCormick. E. (1995) Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms. IEEE Trans. Fuzzy Syst., vol. 3, pp. 129–139.
K-team. (1999) Khepera User Manual. Version 5. 02.
Lee, C.C. (1990) Fuzzy logic control systems: fuzzy logic controller-Part 1 IEEE Trans. System Man Cybernet. SMC-20 404–418.
Lee, K.Y., Lee, D.W. and Sim,. K.B. (2000) Evolutionary neural networks for time series prediction based on 1-system and dna coding method. in: IEEE. Proc. of the 2000 Congress on Evolutionary Computation, Vol. 1. 2, pp. 1467–1474.
Leonard, A. (1994) Molecular Computation of Solutions to Combinatorial Problems. Science, 266, 1021–1024.
Leonard, A. (1996) On constructing a molecular computer. In proceedings of the first DIMACS workshop on DNA computing.
Lipton, R.J. (1995) Using DNA to solve NP-complete problems. Science, 268:542–545, Apr. 28.
Park, D., Kandel, A. and G. Langholz. (1994) Genetic-based new fuzzy reasoning models with application to fuzzy control. IEEE Trans. Syst., Man, Cybern., vol. 24, pp. 39–47.
Surmann, H. (1996) Genetic optimization of a fuzzy system for charging batteries. IEEE Trans. Ind. Electron., vol. 43, pp. 541–548.
Thrift, P. (1991) Fuzzy logic synthesis with genetic algorithms. In proceedings of 4th Int. Conf. on Genetic Algorithms pp 509–513.
Winfree, E. (1995) On The Computational Power of DNA Annealing and Ligation. Technical report, California Institute of Technology, USA.
Wong, C.C. and Feng, S.M. (1995) Switching type fuzzy controller design by genetic algorithm. Fuzzy Sets Syst., vol.74, no. 2, pp. 175C185.
Xiao, P., Prahald, V. and Tong H. L. (2001) DNA coded GA for the rule base optimization of a fuzzy logic controller, in: Proceedings of the 2001 Congress on Evolutionary Computation CEC2001, IEEE Press, COEX, Seoul, Korea, 2001, pp. 1191–1196.
Xiao P., Prahlad, V. and Tong H. L. (2002) Mobile robot obstacle avoidance: DNA coded GA for FLC optimization, in: Proceedings of the Congress on FIRA Robot World Cup 2002, Seoul, Korea, 2002.
Yoshikawa, T. and Uchikawa, Y. (1996) Effect of new mechanism of development from artificial dna and discovery of fuzzy control rules. in: Proc. of IIZUKA’96, pp. 498–501.
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Vadakkepat, P., Peng, X., Heng, L.T. (2004). DNA Coded GA: Rule Base Optimization of FLC for Mobile Robot. In: New Optimization Techniques in Engineering. Studies in Fuzziness and Soft Computing, vol 141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39930-8_20
Download citation
DOI: https://doi.org/10.1007/978-3-540-39930-8_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-05767-0
Online ISBN: 978-3-540-39930-8
eBook Packages: Springer Book Archive