Improving Digital Circuit Synthesis of Complex Functions using Binary Weighted Fitness and Variable Mutation Rate in Cartesian Genetic Programming

Prashanth H. C.; Madhav Rao

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Paper

Improving Digital Circuit Synthesis of Complex Functions using Binary Weighted Fitness and Variable Mutation Rate in Cartesian Genetic Programming

Topics: Applications: Games and Entertainment Technologies, Evolutionary Robotics, Evolutionary Art and Design, Industrial and Real World applications, Computational Economics and Finance; Evolutionary Multi-objective Optimization; Evolutionary Search and Meta-heuristics ; Evolvable/Adaptive Hardware and Systems; Genetic Algorithms; Genetic Programming

In Proceedings of the 14th International Joint Conference on Computational Intelligence - Volume 0IJCCI, 112-120, 2022 , Valletta, Malta

Authors: Prashanth H. C. and Madhav Rao

Affiliation: International Institute of Information Technology Bangalore, India

Keyword(s): CGP, Evolutionary Algorithms, Non-linear Functions, Digital Circuit Synthesis.

Abstract: Cartesian Genetic Programming (CGP) is regularly applied to synthesize and realize digital circuits for small arithmetic and digital-logic functions. CGP benefits in obtaining hardware-efficient circuits through its heuristic search-space exploration, which is hard to reach in a rule-based synthesis process. However, the traditional CGP configuration has limitations in evolving large and complex circuits, requiring a large number of generations. High computation time and energy requirements hinder its usage from evolving complex digital circuits. This paper investigates and demonstrates the desired modifications to existing CGP in the form of Binary Weighted Fitness (BwF) and exponentially varying mutation rate (eVar) to evolve functionally correct solutions extremely fast. The benefits are demonstrated for the basic non-linear power functions and are validated for usage in activation functions which are otherwise difficult to realize. Additionally, 12 different CGP configurations wi th changes in mutation scheme and evolutionary strategy were also investigated for the power functions. A comparison with the benefits of BwF and eVar adopted CGP over the traditional CGP methods is presented and discussed. (More)

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Paper citation in several formats:

H. C., P. and Rao, M. (2022). Improving Digital Circuit Synthesis of Complex Functions using Binary Weighted Fitness and Variable Mutation Rate in Cartesian Genetic Programming. In Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - ECTA; ISBN 978-989-758-611-8; ISSN 2184-3236, SciTePress, pages 112-120. DOI: 10.5220/0011539000003332

@conference{ecta22,
author={Prashanth {H. C.} and Madhav Rao},
title={Improving Digital Circuit Synthesis of Complex Functions using Binary Weighted Fitness and Variable Mutation Rate in Cartesian Genetic Programming},
booktitle={Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - ECTA},
year={2022},
pages={112-120},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011539000003332},
isbn={978-989-758-611-8},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - ECTA
TI - Improving Digital Circuit Synthesis of Complex Functions using Binary Weighted Fitness and Variable Mutation Rate in Cartesian Genetic Programming
SN - 978-989-758-611-8
IS - 2184-3236
AU - H. C., P.
AU - Rao, M.
PY - 2022
SP - 112
EP - 120
DO - 10.5220/0011539000003332
PB - SciTePress