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How noisy data affects geometric semantic genetic programming

Authors:

Luis F. Miranda,

Luiz Otavio V. B. Oliveira,

Joao Francisco B. S. Martins,

Gisele L. PappaAuthors Info & Claims

GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference

Pages 985 - 992

https://doi.org/10.1145/3071178.3071300

Published: 01 July 2017 Publication History

Abstract

Noise is a consequence of acquiring and pre-processing data from the environment, and shows fluctuations from different sources---e.g., from sensors, signal processing technology or even human error. As a machine learning technique, Genetic Programming (GP) is not immune to this problem, which the field has frequently addressed. Recently, Geometric Semantic Genetic Programming (GSGP), a semantic-aware branch of GP, has shown robustness and high generalization capability. Researchers believe these characteristics may be associated with a lower sensibility to noisy data. However, there is no systematic study on this matter. This paper performs a deep analysis of the GSGP performance over the presence of noise. Using 15 synthetic datasets where noise can be controlled, we added different ratios of noise to the data and compared the results obtained with those of a canonical GP. The results show that, as we increase the percentage of noisy instances, the generalization performance degradation is more pronounced in GSGP than GP. However, in general, GSGP is more robust to noise than GP in the presence of up to 10% of noise, and presents no statistical difference for values higher than that in the test bed.

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Cited By

Tonda AAlvarez IMartin SSquillero GLutton ESilva SPaquete L(2023)Towards Evolutionary Control Laws for Viability ProblemsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590415(1464-1472)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590415
Nicolau MAgapitos A(2020)Choosing function sets with better generalisation performance for symbolic regression modelsGenetic Programming and Evolvable Machines10.1007/s10710-020-09391-4Online publication date: 12-May-2020
https://doi.org/10.1007/s10710-020-09391-4
Trujillo LMuñoz LLópez UHernández D(2019)Untapped Potential of Genetic Programming: Transfer Learning and Outlier RemovalGenetic Programming Theory and Practice XVI10.1007/978-3-030-04735-1_10(193-207)Online publication date: 24-Jan-2019
https://doi.org/10.1007/978-3-030-04735-1_10
Show More Cited By

Index Terms

How noisy data affects geometric semantic genetic programming
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by regression
    2. Machine learning approaches
      1. Bio-inspired approaches
        Genetic programming

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cover image ACM Conferences

GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference

July 2017

1427 pages

ISBN:9781450349208

DOI:10.1145/3071178

General Chair:
Peter A. N. Bosman
Centrum Wiskunde & Informatica (CWI)

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

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Published: 01 July 2017

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GECCO '17

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SIGEVO

GECCO '17: Genetic and Evolutionary Computation Conference

July 15 - 19, 2017

Berlin, Germany

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GECCO '17 Paper Acceptance Rate 178 of 462 submissions, 39%;

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

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Cited By

Tonda AAlvarez IMartin SSquillero GLutton ESilva SPaquete L(2023)Towards Evolutionary Control Laws for Viability ProblemsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590415(1464-1472)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590415
Nicolau MAgapitos A(2020)Choosing function sets with better generalisation performance for symbolic regression modelsGenetic Programming and Evolvable Machines10.1007/s10710-020-09391-4Online publication date: 12-May-2020
https://doi.org/10.1007/s10710-020-09391-4
Trujillo LMuñoz LLópez UHernández D(2019)Untapped Potential of Genetic Programming: Transfer Learning and Outlier RemovalGenetic Programming Theory and Practice XVI10.1007/978-3-030-04735-1_10(193-207)Online publication date: 24-Jan-2019
https://doi.org/10.1007/978-3-030-04735-1_10
Oliveira LMartins JMiranda LPappa GTakadama K(2018)Analysing symbolic regression benchmarks under a meta-learning approachProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3208293(1342-1349)Online publication date: 6-Jul-2018
https://dl.acm.org/doi/10.1145/3205651.3208293
López UTrujillo LLegrand P(2018)Filtering Outliers in One Step with Genetic ProgrammingParallel Problem Solving from Nature – PPSN XV10.1007/978-3-319-99253-2_17(209-222)Online publication date: 22-Aug-2018
https://doi.org/10.1007/978-3-319-99253-2_17

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