abstract

SGP-DT: towards effective symbolic regression with a semantic GP approach based on dynamic targets

Authors:

Stefano Ruberto,

Valerio Terragni,

Jason H. MooreAuthors Info & Claims

GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

Pages 25 - 26

https://doi.org/10.1145/3377929.3397486

Published: 08 July 2020 Publication History

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Abstract

Semantic Genetic Programming (SGP) approaches demonstrated remarkable results in different domains. SGP-DT is one of the latest of such approaches. Notably, SGP-DT proposes a dynamic-target approach that combines multiple GP runs without relying on any form of crossover. On eight well-known datasets SGP-DT achieves small RMSE, on average 25% smaller than state-of-the-art approaches.

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Stefano Ruberto, Valerio Terragni, and Jason H. Moore. 2020. SGP-DT: Semantic Genetic Programming Based on Dynamic Targets. In Proceedings of the European Conference on Genetic Programming (EuroGP '20). http://arxiv.org/abs/2001.11535.

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Cited By

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Nadizar GSakallioglu BGarrow FSilva SVanneschi L(2024)Geometric semantic GP with linear scaling: Darwinian versus Lamarckian evolutionGenetic Programming and Evolvable Machines10.1007/s10710-024-09488-025:2Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s10710-024-09488-0
Nadizar GGarrow FSakallioglu BCanonne LSilva SVanneschi LSilva SPaquete L(2023)An Investigation of Geometric Semantic GP with Linear ScalingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590418(1165-1174)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590418
Valentini OAndreadou NBertoldi PLucas ASaviuc IKotsakis E(2022)Demand Response Impact Evaluation: A Review of Methods for Estimating the Customer Baseline LoadEnergies10.3390/en1514525915:14(5259)Online publication date: 20-Jul-2022
https://doi.org/10.3390/en15145259
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Index Terms

SGP-DT: towards effective symbolic regression with a semantic GP approach based on dynamic targets
1. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization
      1. Continuous optimization
        Bio-inspired optimization
      2. Non-parametric optimization
        Genetic programming

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Published In

GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

July 2020

1982 pages

ISBN:9781450371278

DOI:10.1145/3377929

General Chair:
Carlos Artemio Coello Coello
CINVESTAV-IPN

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Published: 08 July 2020

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SIGEVO

GECCO '20: Genetic and Evolutionary Computation Conference

July 8 - 12, 2020

Cancún, Mexico

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Nadizar GSakallioglu BGarrow FSilva SVanneschi L(2024)Geometric semantic GP with linear scaling: Darwinian versus Lamarckian evolutionGenetic Programming and Evolvable Machines10.1007/s10710-024-09488-025:2Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s10710-024-09488-0
Nadizar GGarrow FSakallioglu BCanonne LSilva SVanneschi LSilva SPaquete L(2023)An Investigation of Geometric Semantic GP with Linear ScalingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590418(1165-1174)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590418
Valentini OAndreadou NBertoldi PLucas ASaviuc IKotsakis E(2022)Demand Response Impact Evaluation: A Review of Methods for Estimating the Customer Baseline LoadEnergies10.3390/en1514525915:14(5259)Online publication date: 20-Jul-2022
https://doi.org/10.3390/en15145259
Ruberto STerragni VMoore JKrawiec K(2021)Towards effective GP multi-class classification based on dynamic targetsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3449639.3459324(812-821)Online publication date: 26-Jun-2021
https://dl.acm.org/doi/10.1145/3449639.3459324
Ruberto STerragni VMoore J(2021)A semantic genetic programming framework based on dynamic targetsGenetic Programming and Evolvable Machines10.1007/s10710-021-09419-3Online publication date: 5-Oct-2021
https://doi.org/10.1007/s10710-021-09419-3
Ruberto STerragni VMoore J(2020)Image Feature Learning with Genetic ProgrammingParallel Problem Solving from Nature – PPSN XVI10.1007/978-3-030-58115-2_5(63-78)Online publication date: 2-Sep-2020
https://doi.org/10.1007/978-3-030-58115-2_5

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A semantic genetic programming framework based on dynamic targets

SGP-DT: Semantic Genetic Programming Based on Dynamic Targets

A New Wave: A Dynamic Approach to Genetic Programming

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