Open AccessOn the Analysis of Mutation Operators in Multiobjective Cartesian Genetic Programming for Designing Combinational Logic Circuits
Author(s) -
Lucas Souza,
Heder S. Bernardino
Publication year - 2020
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/eniac.2020.12145
Subject(s) - combinational logic , mutation , computer science , digital electronics , electronic circuit , genetic programming , logic gate , logic synthesis , genetic algorithm , theoretical computer science , mathematical optimization , algorithm , mathematics , engineering , artificial intelligence , machine learning , biochemistry , chemistry , electrical engineering , gene
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.