Shadow Gene Guidance: A Novel Approach for Elevating Genetic Programming Classifications and Boosting Predictive Confidence
Created by W.Langdon from
gp-bibliography.bib Revision:1.7877
- @InProceedings{gharoun:2024:GECCOcomp,
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author = "Hassan Gharoun and Mohammad Sadegh Khorshidi and
Navid Yazdanjue and Fang Chen and Amir H. Gandomi",
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title = "Shadow Gene Guidance: A Novel Approach for Elevating
Genetic Programming Classifications and Boosting
Predictive Confidence",
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booktitle = "GECCO Student Workshop",
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year = "2024",
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editor = "Amir H Gandomi",
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pages = "2095--2098",
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address = "Melbourne, Australia",
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series = "GECCO '24",
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month = "14-18 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, crossover,
uncertainty-aware classification",
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isbn13 = "979-8-4007-0495-6",
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DOI = "
doi:10.1145/3638530.3664175",
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size = "4 pages",
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abstract = "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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notes = "GECCO-2024 Student Workshop A Recombination of the
33rd International Conference on Genetic Algorithms
(ICGA) and the 29th Annual Genetic Programming
Conference (GP)",
- }
Genetic Programming entries for
Hassan Gharoun
Mohammad Sadegh Khorshidi
Navid Yazdanjue
Fang Chen
A H Gandomi
Citations