Cartesian Genetic Programming with a Modified Selection Operator for Combinational Circuit Design: Arithmetic Multipliers and Adders
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0201220" target="_blank" >RIV/00216305:26220/26:0201220 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-031-84356-3_5" target="_blank" >http://dx.doi.org/10.1007/978-3-031-84356-3_5</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-84356-3_5" target="_blank" >10.1007/978-3-031-84356-3_5</a>
Alternative languages
Result language
angličtina
Original language name
Cartesian Genetic Programming with a Modified Selection Operator for Combinational Circuit Design: Arithmetic Multipliers and Adders
Original language description
The evolutionary design of combinational logic circuits offers an innovative approach that often surpasses traditional methods, such as the Quine-McCluskey algorithm, in both efficiency and effectiveness. Cartesian Genetic Programming (CGP) emerges as a potent technique in this domain, enabling versatile circuit designs tailored to diverse requirements such as cost, gate count, and circuit speed. In this paper, we introduce an advanced modification of CGP, termed CGP-SA, which integrates the Simulated Annealing mechanism into the selection operator. This novel approach enhances the algorithm's ability to escape local optima, thereby fostering the discovery of more optimal solutions. We demonstrate the efficacy of CGP-SA through the design of three types of multipliers and two types of adders, utilizing diverse logic gate sets. This exploration not only reveals the flexibility of CGP-SA in handling various circuit design challenges but also highlights its adaptability to different optimization criteria.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
<a href="/en/project/GA24-12474S" target="_blank" >GA24-12474S: Benchmarking derivative-free global optimization methods</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Lecture Notes in Artificial Intelligence
ISBN
978-3-031-84355-6
ISSN
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e-ISSN
1611-3349
Number of pages
13
Pages from-to
53-65
Publisher name
Springer Nature
Place of publication
CHAM
Event location
Zakopane, Poland
Event date
Jun 16, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001535049000005