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.

EVOLVING DIGITAL CIRCUITS USING HYBRID PARTICLE SWARM OPTIMIZATION AND DIFFERENTIAL EVOLUTION

2006 ◽  
Vol 16 (03) ◽  
pp. 163-177 ◽  
Author(s):  
PHILLIP W. MOORE ◽  
GANESH K. VENAYAGAMOORTHY
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Hybrid Algorithm ◽  
Particle Swarm ◽  
Logic Gates ◽  
Logic Circuits ◽  
Combinational Logic ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization ◽  
Combinational Logic Circuits

This paper presents the evolution of combinational logic circuits by a new hybrid algorithm known as the Differential Evolution Particle Swarm Optimization (DEPSO), formulated from the concepts of a modified particle swarm and differential evolution. The particle swarm in the hybrid algorithm is represented by a discrete 3-integer approach. A hybrid multi-objective fitness function is coined to achieve two goals for the evolution of circuits. The first goal is to evolve combinational logic circuits with 100% functionality, called the feasible circuits. The second goal is to minimize the number of logic gates needed to realize the feasible circuits. In addition, the paper presents modifications to enhance performance and robustness of particle swarm and evolutionary techniques for discrete optimization problems. Comparison of the performance of the hybrid algorithm to the conventional Karnaugh map and evolvable hardware techniques such as genetic algorithm, modified particle swarm, and differential evolution are presented on a number of case studies. Results show that feasible circuits are always achieved by the DEPSO algorithm unlike with other algorithms and the percentage of best solutions (minimal logic gates) is higher.

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