Abstract
This paper proposes a multiobjective Cartesian Genetic Programming with an adaptive population size to design approximate digital circuits via evolutionary algorithms, analyzing the trade-off between the most often used objectives: error, area, power dissipation, and delay. Combinational digital circuits such as adders, multipliers, and arithmetic logic units (ALUs) with up to 16 inputs and 370 logic gates are considered in the computational experiments. The proposed method was able to produce approximate circuits with good operational characteristics when compared with other methods from the literature.
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The symbol “\(\setminus \)” represents the operator of set difference.
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The source-code of CGPMO+APS is available at https://github.com/ciml.
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Acknowledgments
We thanks the support provided by CNPq (312337/2017-5 and 312682/2018-2), FAPEMIG (APQ-00337-18), PPGCC, and PPGMC.
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Lima, L.S., Bernardino, H.S., Barbosa, H.J.C. (2019). Designing Combinational Circuits Using a Multi-objective Cartesian Genetic Programming with Adaptive Population Size. In: Nicosia, G., Pardalos, P., Umeton, R., Giuffrida, G., Sciacca, V. (eds) Machine Learning, Optimization, and Data Science. LOD 2019. Lecture Notes in Computer Science(), vol 11943. Springer, Cham. https://doi.org/10.1007/978-3-030-37599-7_49
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DOI: https://doi.org/10.1007/978-3-030-37599-7_49
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