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2019 | OriginalPaper | Buchkapitel

Cartesian Genetic Programming with Guided and Single Active Mutations for Designing Combinational Logic Circuits

verfasst von : José Eduardo H. da Silva, Lucas A. M. de Souza, Heder S. Bernardino

Erschienen in: Machine Learning, Optimization, and Data Science

Verlag: Springer International Publishing

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Abstract

The design of digital circuits using Cartesian Genetic Programming (CGP) has been widely investigated but the evolution of complex combinational logic circuits is a hard task for CGP. We introduce here a new mutation operator for CGP that aims to reduce the number of evaluations needed to find a feasible solution by modifying the subgraph of the worst output of the candidate circuits. Also, we propose a variant of the standard evolutionary strategy commonly adopted in CGP, where (i) the Single Active Mutation (SAM) and (ii) the proposed mutation operator is used in order to improve the capacity of CGP in generating feasible circuits. The proposals are applied to a benchmark of combinational logic circuits with multiple outputs and the results obtained are compared to those found by a CGP with SAM. The main advantages observed when both mutation operators are combined are the reduction of the number of objective function evaluations required to find a feasible solution and the improvement in the success rate.

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Vorheriges Kapitel Combinatorial Learning in Traffic Management

Nächstes Kapitel Designing an Optimal and Resilient iBGP Overlay with Extended ORRTD

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Titel: Cartesian Genetic Programming with Guided and Single Active Mutations for Designing Combinational Logic Circuits
verfasst von: José Eduardo H. da Silva
Lucas A. M. de Souza
Heder S. Bernardino
Verlag: Springer International Publishing
Buch: Machine Learning, Optimization, and Data Science
Print ISBN: 978-3-030-37598-0

Electronic ISBN: 978-3-030-37599-7

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-37599-7_33

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