On the Analysis of Mutation Operators in Multiobjective Cartesian Genetic Programming for Designing Combinational Logic Circuits

  • Lucas Souza UFJF
  • Heder Bernardino UFJF


Approximate Computing is an emerging paradigm that takes advantage of inherently error resilient digital circuits to design circuits with higher energetic efficiency, lower delay, or a smaller occupied area on the chips. Traditional approaches do not handle multiple objectives and metaheuristics appear as a proper alternative. In particular, Multiobjective Cartesian Genetic Programming (MOCGP) can find good solutions to the design and optimization of approximate circuits. The performance of CGP depends on the mutation adopted, as normally CGP only uses mutation for creating new solutions. However, to the best of our knowledge, just the traditional point mutation (PM) was used by the previously proposed MOCGP. Thus, the literature lacks an analysis of the best mutation operators of MOCGP. We propose here the analysis of PM and Single Active Mutation(SAM) on the multiobjective optimization of 15 heterogeneous combinational logic circuits from scratch and starting with a feasible solution. The results indicate that SAM obtained better results than PM.

Palavras-chave: Multiobjective Cartesian Genetic Programming, Mutation Operator and Combinational Logic Circuits


Ansari, M. S., Mrazek, V., Cockburn, B. F., Sekanina, L., Vasicek, Z., and Han, J. (2019). Improving the accuracy and hardware efficiency of neural networks using approximate multipliers. IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

da Silva, J. E. H., de Souza, L. A., and Bernardino, H. S. (2019). Cartesian genetic programming with guided and single active mutations for designing combinational logic circuits. In International Conference on Machine Learning, Optimization, and Data Science, pages 396–408. Springer. de Souza, L. A. M., da Silva, J. E. H., Chaves, L. J., and Bernardino, H. S. (2020). A benchmark suite for designing combinational logic circuits via metaheuristics. Applied Soft Computing, page 106246.

Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. on Evol. Computation, 6(2):182–197.

Fonseca, C. M., Paquete, L., and López-Ibánez, M. (2006). An improved dimensionsweep algorithm for the hypervolume indicator. In 2006 IEEE international conference on evolutionary computation, pages 1157–1163. IEEE.

Goldman, B. W. and Punch, W. F. (2013). Reducing wasted evaluations in cartesian genetic programming. In European Conference on Genetic Programming, pages 61– 72. Springer.

Hrbacek, R., Mrazek, V., and Vasicek, Z. (2016). Automatic design of approximate circuits by means of multi-objective evolutionary algorithms. In Intl. Conf. on Design and Technology of Integrated Systems in Nanoscale Era (DTIS), pages 1–6. IEEE.

Koza, J. R. (1992). Genetic programming: on the programming of computers by means of natural selection, volume 1. MIT press.

Lima, L. S., Bernardino, H. S., and Barbosa, H. J. (2019). Designing combinational circuits using a multi-objective cartesian genetic programming with adaptive population size. In International Conference on Machine Learning, Optimization, and Data Science, pages 592–604. Springer.

Miller, J. F. (2011). Cartesian genetic programming. In Cartesian Genetic Programming, pages 17–34. Springer.

Miller, J. F. and Thomson, P. (2000). Cartesian genetic programming. Proc. of EuroGP 2000, 1802:121–132.

Mrazek, V., Sekanina, L., and Vasicek, Z. (2020). Using libraries of approximate circuits in design of hardware accelerators of deep neural networks. In Proc. of the Intl. Conf. on Artificial Intelligence Circuits and Systems (AICAS), pages 243–247. IEEE.

Oliveira, J. R., Soares, L. B., Costa, E., and Bampi, S. (2015). Energy-efficient gaussian filter for image processing using approximate adder circuits. In 2015 IEEE International Conference on Electronics, Circuits, and Systems (ICECS), pages 450–453. IEEE.

Vasicek, Z. (2018). Bridging the gap between evolvable hardware and industry using cartesian genetic programming. In Inspired by Nature, pages 39–55. Springer.

Vasicek, Z. and Sekanina, L. (2014). How to evolve complex combinational circuits from scratch? In Proc. of the Intl. Conf. on Evolvable Systems, pages 133–140. IEEE.

Zitzler, E. and Thiele, L. (1998). Multiobjective optimization using evolutionary algorithms—a comparative case study. In International conference on parallel problem solving from nature, pages 292–301. Springer.
Como Citar

Selecione um Formato
SOUZA, Lucas; BERNARDINO, Heder. On the Analysis of Mutation Operators in Multiobjective Cartesian Genetic Programming for Designing Combinational Logic Circuits. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 17. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 390-401. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2020.12145.