Tree-based genetic programming for evolutionary analog circuit with approximate Shapley value

Xinming Shi, Leandro L. Minku, Xin Yao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The automated design of analog circuits presents a significant challenge due to the complexity of circuit topology and parameter selection. Traditional evolutionary algorithms, such as Genetic Programming (GP), have shown potential in this domain but are often hindered by inefficient search processes and the large design space. Furthermore, fitness evaluation in the evolutionary design of circuits is often computationally very expensive. In this paper, we introduce a novel evolutionary framework that leverages approximate Shapley values to guide the optimization process in tree-based genetic programming for analog circuit design. Our approach addresses the computational challenges associated with computing Shapley values by introducing a two-stage evolutionary framework that includes a Shapley Value Library (SVlib) and a KNNbased prediction for efficient estimation of Shapley values. Our proposed work not only enhances the search efficiency by focusing on the most beneficial subcircuits but also leads to more compact and efficient circuit designs. Furthermore, fitness evaluation in the evolutionary design of circuits is often computationally very expensive experiments, we verify that our framework accelerates evolutionary convergence and outperforms traditional methods in terms of circuit optimization.
Original languageEnglish
Title of host publicationForty-fourth SGAI International Conference on Artificial Intelligence: Proceedings
PublisherSpringer
Pages253-267
Number of pages15
DOIs
Publication statusPublished - 29 Nov 2024
Event44th SGAI International Conference on Artificial Intelligence 2024 - Cambridge, United Kingdom
Duration: 17 Dec 202419 Dec 2024

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameLecture Notes in Artificial Intelligence
ISSN (Print)2945-9133
ISSN (Electronic)2945-9141

Conference

Conference44th SGAI International Conference on Artificial Intelligence 2024
Abbreviated titleAI 2024
Country/TerritoryUnited Kingdom
Period17/12/202419/12/2024

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