Feature engineering for improving robustness of crossover in symbolic regression

Aliyu Sani Sambo; R. Muhammad Atif Azad; Yevgeniya Kovalchuk; Vivek Padmanaabhan Indramohan; Hanifa Shah

doi:10.1145/3377929.3390078

Feature engineering for improving robustness of crossover in symbolic regression

Aliyu Sani Sambo, R. Muhammad Atif Azad, Yevgeniya Kovalchuk, Vivek Padmanaabhan Indramohan, Hanifa Shah

Faculty of Computing, Engineering and the Built Environment

Birmingham City University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Original language	English
Title of host publication	GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
Publisher	Association for Computing Machinery
Pages	249-250
Number of pages	2
ISBN (Electronic)	9781450371278
DOIs	https://doi.org/10.1145/3377929.3390078
Publication status	Published (VoR) - 8 Jul 2020
Event	2020 Genetic and Evolutionary Computation Conference, GECCO 2020 - Cancun, Mexico Duration: 8 Jul 2020 → 12 Jul 2020

Publication series

Name	GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

Conference

Conference	2020 Genetic and Evolutionary Computation Conference, GECCO 2020
Country/Territory	Mexico
City	Cancun
Period	8/07/20 → 12/07/20

Keywords

Feature engineering
Genetic programming
Regression

Access to Document

10.1145/3377929.3390078

Cite this

Sambo, A. S., Azad, R. M. A., Kovalchuk, Y., Indramohan, V. P., & Shah, H. (2020). Feature engineering for improving robustness of crossover in symbolic regression. In GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (pp. 249-250). (GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion). Association for Computing Machinery. https://doi.org/10.1145/3377929.3390078

Sambo, Aliyu Sani ; Azad, R. Muhammad Atif ; Kovalchuk, Yevgeniya et al. / Feature engineering for improving robustness of crossover in symbolic regression. GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, 2020. pp. 249-250 (GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion).

@inproceedings{f22b1018598d4a9db6f887316be76547,

title = "Feature engineering for improving robustness of crossover in symbolic regression",

keywords = "Feature engineering, Genetic programming, Regression",

author = "Sambo, {Aliyu Sani} and Azad, {R. Muhammad Atif} and Yevgeniya Kovalchuk and Indramohan, {Vivek Padmanaabhan} and Hanifa Shah",

note = "Publisher Copyright: {\textcopyright} 2020 Owner/Author.; 2020 Genetic and Evolutionary Computation Conference, GECCO 2020 ; Conference date: 08-07-2020 Through 12-07-2020",

year = "2020",

month = jul,

day = "8",

doi = "10.1145/3377929.3390078",

language = "English",

series = "GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion",

publisher = "Association for Computing Machinery",

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booktitle = "GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion",

}

Sambo, AS, Azad, RMA, Kovalchuk, Y, Indramohan, VP & Shah, H 2020, Feature engineering for improving robustness of crossover in symbolic regression. in GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, pp. 249-250, 2020 Genetic and Evolutionary Computation Conference, GECCO 2020, Cancun, Mexico, 8/07/20. https://doi.org/10.1145/3377929.3390078

Feature engineering for improving robustness of crossover in symbolic regression. / Sambo, Aliyu Sani; Azad, R. Muhammad Atif; Kovalchuk, Yevgeniya et al.
GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, 2020. p. 249-250 (GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Feature engineering for improving robustness of crossover in symbolic regression

AU - Sambo, Aliyu Sani

AU - Azad, R. Muhammad Atif

AU - Kovalchuk, Yevgeniya

AU - Indramohan, Vivek Padmanaabhan

AU - Shah, Hanifa

PY - 2020/7/8

Y1 - 2020/7/8

KW - Feature engineering

KW - Genetic programming

KW - Regression

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DO - 10.1145/3377929.3390078

M3 - Conference contribution

AN - SCOPUS:85089751695

T3 - GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

SP - 249

EP - 250

BT - GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

PB - Association for Computing Machinery

T2 - 2020 Genetic and Evolutionary Computation Conference, GECCO 2020

Y2 - 8 July 2020 through 12 July 2020

ER -

Sambo AS, Azad RMA, Kovalchuk Y, Indramohan VP, Shah H. Feature engineering for improving robustness of crossover in symbolic regression. In GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery. 2020. p. 249-250. (GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion). doi: 10.1145/3377929.3390078

Feature engineering for improving robustness of crossover in symbolic regression

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