DNA Coded GA: Rule Base Optimization of FLC for Mobile Robot

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).

[1]  L M Adleman,et al.  Molecular computation of solutions to combinatorial problems. , 1994, Science.

[2]  Erik Winfree,et al.  On the computational power of DNA annealing and ligation , 1995, DNA Based Computers.

[3]  Leonard M. Adleman,et al.  On constructing a molecular computer , 1995, DNA Based Computers.

[4]  E B Baum,et al.  Building an associative memory vastly larger than the brain. , 1995, Science.

[5]  Richard J. Lipton,et al.  Breaking DES using a molecular computer , 1995, DNA Based Computers.

[6]  Richard J. Lipton,et al.  On the Computational Power of DNA , 1996, Discret. Appl. Math..

[7]  Donald Beaver,et al.  A universal molecular computer , 1995, DNA Based Computers.

[8]  Xiao Peng,et al.  DNA coded GA for the rule base optimization of a fuzzy logic controller , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[9]  Detlef Nauck,et al.  Fuzzy-Evolutionary Systems , 1998 .

[10]  Ching-Chang Wong,et al.  Switching-type fuzzy controller design by genetic algorithms , 1995, Fuzzy Sets Syst..

[11]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[12]  Witold Pedrycz,et al.  Fuzzy evolutionary computation , 1997 .

[13]  Philip R. Thrift,et al.  Fuzzy Logic Synthesis with Genetic Algorithms , 1991, ICGA.

[14]  Abdollah Homaifar,et al.  Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms , 1995, IEEE Trans. Fuzzy Syst..

[15]  R J Lipton,et al.  DNA solution of hard computational problems. , 1995, Science.

[16]  Zbigniew Michalewicz,et al.  Handbook of Evolutionary Computation , 1997 .

[17]  G. Langholz,et al.  Genetic-Based New Fuzzy Reasoning Models with Application to Fuzzy Control , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[18]  Hartmut Surmann,et al.  Genetic optimization of a fuzzy system for charging batteries , 1996, IEEE Trans. Ind. Electron..