JACIII Vol.11 No.9 pp. 1122-1127
doi: 10.20965/jaciii.2007.p1122


Computational Intelligence in Circuit Synthesis

Cecília Reis and J. A. Tenreiro Machado

Institute of Engineering of Porto / GECAD, Rua Dr. António Bernardino de Almeida, Porto, Portugal

March 13, 2007
June 14, 2007
November 20, 2007
computational intelligence, evolutionary algorithms, swarm intelligence, logic circuits design

This paper is devoted to the synthesis of combinational logic circuits through computational intelligence or, more precisely, using evolutionary computation techniques. Are studied two evolutionary algorithms, the Genetic and the Memetic Algorithm (GAs, MAs) and one swarm intelligence algorithm, the Particle Swarm Optimization (PSO). GAs are optimization and search techniques based on the principles of genetics and natural selection. MAs are evolutionary algorithms that include a stage of individual optimization as part of its search strategy, being the individual optimization in the form of a local search. The PSO is a population-based search algorithm that starts with a population of random solutions called particles. This paper presents the results for digital circuits design using the three above algorithms. The results show the statistical characteristics of this algorithms with respect to the number of generations required to achieve the solutions. The article analyzes also a new fitness function that includes an error discontinuity measure, which demonstrated to improve significantly the performance of the algorithm.

Cite this article as:
Cecília Reis and J. A. Tenreiro Machado, “Computational Intelligence in Circuit Synthesis,” J. Adv. Comput. Intell. Intell. Inform., Vol.11, No.9, pp. 1122-1127, 2007.
Data files:
  1. [1] S. Louis and G. Rawlins, “Designer Genetic Algorithms: Genetic Algorithms in Structure Design,” Proc. of the Fourth Int. Conf. on Genetic Algorithms, 1991.
  2. [2] D. E. Goldberg, “Genetic Algorithms in Search Optimization and Machine Learning,” Addison-Wesley, 1989.
  3. [3] R. S. Zebulum, M. A. Pacheco, and M. M. Vellasco, “Evolutionary Electronics: Automatic Design of Electronic Circuits and Systems by Genetic Algorithms,” CRC Press, 2001.
  4. [4] J. R. Koza, “Genetic Programming,” On the Programming of Computers by means of Natural Selection, MIT Press, 1992.
  5. [5] C. A. Coello, A. D. Christiansen, and A. H. Aguirre, “Using Genetic Algorithms to Design Combinational Logic Circuits,” Intelligent Engineering through Artificial Neural Networks, Vol.6, pp. 391-396, 1996.
  6. [6] C. Reis, J. A. T. Machado, and J. B. Cunha, “Evolutionary Design of Combinational Logic Circuits,” Journal of Advanced Computational Intelligence and Intelligent Informatics, Fuji Technology Press, Vol.8, No.5, pp. 507-513, Sept. 2004.
  7. [7] C. Reis, J. A. T. Machado, and J. B. Cunha, “Evolutionary design of combinational circuits using fractional-order fitness,” Proc. of the Fifth EUROMECH Nonlinear Dynamics Conference, pp. 1312-1321, 2005.
  8. [8] C. Reis, J. A. T. Machado, and J. B. Cunha, “An Evolutionary Hybrid Approach in the Design of Combinational Digital Circuits,” WSEAS Transactions on Systems, Issue 12, Vol.4, pp. 2338-2345.
  9. [9] P. Moscato, “On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms,” Tech. Rep. Caltech Concurrent Computation Program, Report. 826, California Institute of Technology, Pasadena, California, USA, 1989.
  10. [10] N. Krasnogor, “Studies on the Theory and Design Space of Memetic Algorithms,” Ph.D. thesis, University of the West of England, Bristol, June, 2002.
  11. [11] P. Merz and B. Freisleben, “A Comparison of Memetic Algorithms,” Tabu Search, and Ant Colonies for the Quadratic Assignment Problem, Proc. of the 1999 Congress on Evolutionary Computation, IEEE Press, pp. 2063-2070, 1999.
  12. [12] R. Dawkins, “The Selfish Gene,” Oxford University Press, New York, 1976.
  13. [13] Y. S. Ong and A. J. Keane, “Meta-Lamarckian in Memetic Algorithm,” IEEE Transactions On Evolutionary Computation, Vol.8, No.2, pp. 99-110, April, 2004.
  14. [14] J. Kennedy and R. C. Eberhart, “Particle Swarm Optimization,” Proc. of the IEEE Int. Conf. Neural Networks, pp. 1942-1948, 1995.
  15. [15] Y. Shi and R. C. Eberhart, “A Modified Particle Swarm Optimizer,” Proc. of the 1998 Int. Conf. on Evolutionary Computation, pp. 69-73, 1998.
  16. [16] M. Clerc and J. Kennedy, “The Particle Swarm: explosion, stability, and convergence in a multi-dimensional complex space,” IEEE Trans. on Evolutionary Comp., Vol.6, pp. 58-73, 2002.

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