Shadow Gene Guidance: A Novel Approach for Elevating Genetic Programming Classifications and Boosting Predictive Confidence

Gharoun, H; Khorshidi, MS; Yazdanjue, N; Chen, F; Gandomi, AH

Shadow Gene Guidance: A Novel Approach for Elevating Genetic Programming Classifications and Boosting Predictive Confidence

Gharoun, H Khorshidi, MS Yazdanjue, N Chen, F

Gandomi, AH

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Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Conference Proceeding
Citation:: GECCO 2024 Companion - Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion, 2024, pp. 2095-2098
Issue Date:: 2024-07-14

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Field	Value	Language
dc.contributor.author	Gharoun, H
dc.contributor.author	Khorshidi, MS
dc.contributor.author	Yazdanjue, N
dc.contributor.author	Chen, F https://orcid.org/0000-0003-4971-8729
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2024-10-10T01:41:37Z
dc.date.available	2024-10-10T01:41:37Z
dc.date.issued	2024-07-14
dc.identifier.citation	GECCO 2024 Companion - Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion, 2024, pp. 2095-2098
dc.identifier.uri	http://hdl.handle.net/10453/181367
dc.description.abstract	This paper introduces a novel classification method that utilizes genetic programming (GP). The primary purpose of the proposed method is to enhance future generations of GP, through continuously refining the genetic makeup of the population for improved classification results. Accordingly, this paper developed the novel method by modifying Boruta feature selection method in such a way that allows to evaluate the significance of individuals' genes. This method creates modified versions of the genes called "shadow genes", evaluates their impact on model performance in competing with shadow genes, and identifies key genes. These key genes are then used to enhance future generations. The obtained results demonstrated that the proposed method not only enhances the fitness of the individuals but also steers the population toward optimal solutions. Furthermore, empirical validation on multiple datasets reveals that the proposed method significantly outperforms classic GP models in both accuracy and reduced prediction entropy, showcasing its superior ability to generate confident and reliable predictions.
dc.language	en
dc.publisher	Association for Computing Machinery (ACM)
dc.relation	http://purl.org/au-research/grants/arc/DE210101808
dc.relation.ispartof	GECCO 2024 Companion - Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion
dc.relation.ispartof	Proceedings of the Genetic and Evolutionary Computation Conference Companion
dc.relation.isbasedon	10.1145/3638530.3664175
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Shadow Gene Guidance: A Novel Approach for Elevating Genetic Programming Classifications and Boosting Predictive Confidence
dc.type	Conference Proceeding
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Data Science Institute (DSI)
pubs.organisational-group	University of Technology Sydney/Strength - DSI - Data Science Institute
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2024-10-10T01:41:36Z
pubs.publication-status	Published

Abstract:

This paper introduces a novel classification method that utilizes genetic programming (GP). The primary purpose of the proposed method is to enhance future generations of GP, through continuously refining the genetic makeup of the population for improved classification results. Accordingly, this paper developed the novel method by modifying Boruta feature selection method in such a way that allows to evaluate the significance of individuals' genes. This method creates modified versions of the genes called "shadow genes", evaluates their impact on model performance in competing with shadow genes, and identifies key genes. These key genes are then used to enhance future generations. The obtained results demonstrated that the proposed method not only enhances the fitness of the individuals but also steers the population toward optimal solutions. Furthermore, empirical validation on multiple datasets reveals that the proposed method significantly outperforms classic GP models in both accuracy and reduced prediction entropy, showcasing its superior ability to generate confident and reliable predictions.

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http://hdl.handle.net/10453/181367