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ABSTRACT

Evolutionary design of circuits (EDC), an important branch of evolvable hardware which emphasizes circuit design, is a promising way to realize automated design of electronic circuits. In order to improve the evolutionary design of logic circuits in a more efficient, scalable and capable way, an Adaptive Immune Genetic Algorithm (AIGA) was designed. The AIGA draws into the mechanisms in biological immune systems such as clonal selection, hypermutation, and immune memory. Besides, the AIGA features an adaptation strategy that enables crossover probability and mutation probability to vary with genetic-search process. Our results are compared with those produced by the Multi-objective Evolutionary Algorithm (MOEA) and the Simple Immune Algorithm (SIA). The simulation results show that AIGA overcomes the disadvantages of premature convergence, and improves the global searching efficiency and capability.

REFERENCES

• 1: Thompson, A. 1996 Hardware Evolution: Automatic design of electronic circuits in reconfigurable hardware by artificial evolution. Doctoral Thesis. University of Sussex.
• 2: Rui Liu , Sang-you Zeng , Lixin Ding , Lishan Kang , Hui Li , Yuping Chen , Yong Liu , Yueping Han, An Efficient Multi-Objective Evolutionary Algorithm for Combinational Circuit Design, Proceedings of the first NASA/ESA conference on Adaptive Hardware and Systems, p.215-221, June 15-18, 2006 [doi>10.1109/AHS.2006.21]
• 3: Nadia Nedjah , Luiza de Macedo Mourelle, A comparison of two circuit representations for evolutionary digital circuit design, Proceedings of the 17th international conference on Innovations in applied artificial intelligence, p.594-604, May 17-20, 2004, Ottawa, Canada [doi>10.1007/b97304]
• 4: Julian F. Miller , Dominic Job , Vesselin K. Vassilev, Principles in the Evolutionary Design of Digital Circuits—Part II, Genetic Programming and Evolvable Machines, v.1 n.3, p.259-288, July 2000 [doi>10.1023/A:1010066330916]
• 5: Yong-Sheng Ding , Zhi-Hua Hu , Hong-Bin Sun, An antibody network inspired evolutionary framework for distributed object computing, Information Sciences: an International Journal, v.178 n.24, p.4619-4631, December, 2008 [doi>10.1016/j.ins.2008.08.010]
• 6: Hu, Z. H., Ding, Y.S and Shao, Q. 2008 Immune co-evolutionary algorithm based partition balancing optimization for tobacco distribution system, Expert Systems with Applications, 42(4):250--258
• 7: Caihong Hou , Yongsheng Ding , Xianyi Zeng, Immune-based evolutionary algorithm for fabric evaluation, Mathematics and Computers in Simulation, v.77 n.5-6, p.540-549, May, 2008 [doi>10.1016/j.matcom.2007.11.019]
• 8: Dai, Y. S., Li, Y. Y. 2007 Adaptive immune-genetic algorithm for global optimization to multivariable function. Journal of Systems Engineering and Electronics, 18(3): 655--660.
• 9: Wang, X. F., Zhang, X.J., Cao, X. B., Zhang, J. and Feng, L. 1999 An Improved Genetic Algotithm Based on Immune Principle. Mini--Micro System, 2(20):117--120.
• 10: Louis, S. J., Rawlins, G.J. 1991 Using genetic algorithms to design structures. Technical Report 326, Computer Science Department, Indiana University, Bloomington.
• 11: Srinivas, M., Patnaik, L. M. 1994 Adaptive Probabilties of Crossover and Mutation in Genetic Algorithm. IEEE Transaction On Systems Man and Cybernetics, 24(4):656--667.
• 12: Gary B. Lamont , David A. Van Veldhuizen, Evolutionary Algorithms for Solving Multi-Objective Problems, Kluwer Academic Publishers, Norwell, MA, 2002
• 13: Zhang, Y. G., Luo, W. J., Wang, X. F. 2006 Optimization design based on genetic algorithm of immunity. Computer Engineering and Application,11(1):38--40.

INDEX TERMS

Primary Classification:
  B. Hardware
  B.2 ARITHMETIC AND LOGIC STRUCTURES
      B.2.4 High-speed Arithmetic

General Terms:
Algorithms, Design, Theory

Keywords:
evolutionary design of circuits, evolvable hardware, genetic algorithm, immune genetic algorithm, logic circuit design