A Reinforcement Learning Based Adaptive Mutation for Cartesian Genetic Programming Applied to the Design of Combinational Logic Circuits

2020 ◽  
pp. 18-32
Author(s):  
Frederico José Dias Möller ◽  
Heder Soares Bernardino ◽  
Luciana Brugiolo Gonçalves ◽  
Stênio Sã Rosário Furtado Soares
Keyword(s):  
Reinforcement Learning ◽  
Genetic Programming ◽  
Logic Circuits ◽  
Adaptive Mutation ◽  
Combinational Logic ◽  
Cartesian Genetic Programming ◽  
Combinational Logic Circuits

scholarly journals Biased Mutation and Tournament Selection Approaches for Designing Combinational Logic Circuits via Cartesian Genetic Programming

2018 ◽  
Author(s):  
José Eduardo H. Da Silva ◽  
Francisco A. L. Manfrini ◽  
Heder S. Bernardino ◽  
Helio J. C. Barbosa
Keyword(s):  
Genetic Programming ◽  
Objective Function ◽  
Logic Gates ◽  
Logic Circuits ◽  
Combinational Logic ◽  
Cartesian Genetic Programming ◽  
Mutation Strategy ◽  
Tournament Selection ◽  
Combinational Logic Circuits ◽  
Better Than

Cartesian Genetic Programming (CGP) is often applied to design combinational logic circuits. However, there is no consensus in the literature regarding the more appropriate objective function when it is desired to minimize the number of logic gates of the circuit. Thus, we analyze here two strategies: the minimization of the number of logic gates and the maximization of the number of wire gates. Additionally, a biased mutation strategy for CGP, which were previously presented and tested only to find a feasible solution, are extended in this paper for the subsequent optimization step. Several configurations were proposed and tested varying objective function and selection schemes. Compu- tational experiments are conducted with some benchmark circuits to relatively compare the proposed methods, and the results obtained are better than those found by the other techniques considered here.

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

2020 ◽  
Author(s):  
Lucas Souza ◽  
Heder Bernardino
Keyword(s):  
Genetic Programming ◽  
Logic Circuits ◽  
Energetic Efficiency ◽  
Approximate Computing ◽  
Combinational Logic ◽  
Cartesian Genetic Programming ◽  
Design And Optimization ◽  
Mutation Operators ◽  
Combinational Logic Circuits ◽  
Traditional Approaches

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.

Timing and Area Optimization of CMOS Combinational-Logic Circuits Accounting for Total-Dose Radiation Effects

1987 ◽  
Vol 34 (6) ◽  
pp. 1386-1391 ◽  
Author(s):  
R. S. Gyurcsik ◽  
D. W. Thomas ◽  
R. H. Gallimore ◽  
B. L. Bhuva ◽  
S. E. Kerns
Keyword(s):  
Total Dose ◽  
Radiation Effects ◽  
Logic Circuits ◽  
Combinational Logic ◽  
Combinational Logic Circuits ◽  
Area Optimization ◽  
Dose Radiation
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