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Different mutation and crossover set of genetic programming in an automated machine learning

IAES International Journal of Artificial Intelligence · Vol. 9 No. 3 (2020) · pp. 402–408

DOI
10.11591/ijai.v9.i3.pp402-408
ISSN
2252-8938, 2089-4872
Published
2020-09-01
Publisher
Institute of Advanced Engineering and Science
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Automated machine learning is a promising approach widely used to solve classification and prediction problems, which currently receives much attention for modification and improvement. One of the progressing works for automated machine learning improvement is the inclusion of evolutionary algorithm such as Genetic Programming. The function of Genetic Programming is to optimize the best combination of solutions from the possible pipelines of machine learning modelling, including selection of algorithms and parameters optimization of the selected algorithm. As a family of evolutionary based algorithm, the effectiveness of Genetic Programming in providing the best machine learning pipelines for a given problem or dataset is substantially depending on the algorithm parameterizations including the mutation and crossover rates. This paper presents the effect of different pairs of mutation and crossover rates on the automated machine learning performances that tested on different types of datasets. The finding can be used to support the theory that higher crossover rates used to improve the algorithm accuracy score while lower crossover rates may cause the algorithm to converge at earlier stage.

Keywords

Collaboration network of the authors

29 authors · 82 co-authorship links

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universiti Teknologi MARA
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Rahayu Abdul RahmanAffiliation unknown4
Thuraiya MohdAffiliation unknown3
Teoh Sian HoonAffiliation unknown3
Nadirah OthmanAffiliation unknown3
Nor Aslily SarkamAffiliation unknown3
Nor Faezah Mohamad RaziAffiliation unknown3
Nor Faezah Mohamad RaziAffiliation unknown2
Mohamed Imran Mohamed AriffUniversiti Teknologi MARA1
Samsiah Binti AhmadUniversiti Teknologi MARA1
Mohammad AbdullahUniversiti Teknologi MARA1
Nur Anim Abdul HalimAffiliation unknown1
Anis Zafirah AzmiUniversiti Teknologi MARA1
Siti Khatijah Nor Abdul RahimAffiliation unknown1
Raseeda HamzahAffiliation unknown1
Megat Nabil Irwan Megat AmerudinAffiliation unknown1
Mohd Suffian SulaimanAffiliation unknown1
Amir Hamzah JaafarAffiliation unknown1
Anis Zulaikha Mohd ZukriAffiliation unknown1
Siti Rasidah Md SakipAffiliation unknown1
Syafiqah JamilAffiliation unknown1
Maheran ZakariaAffiliation unknown1
Rahayu Abdul RahmanAffiliation unknown1
Normah OmarAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Different mutation and crossover set of genetic programming in an automated machine learning</dc:title>
	<dc:creator>Masrom, Suraya</dc:creator>
	<dc:creator>Mohamad, Masurah</dc:creator>
	<dc:creator>Mohamed Hatim, Shahirah</dc:creator>
	<dc:creator>Baharun, Norhayati</dc:creator>
	<dc:creator>Omar, Nasiroh</dc:creator>
	<dc:creator>Abd. Rahman, Abdullah Sani</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Automated machine learning, Classification, Crossover, Genetic programming, Mutation</dc:subject>
	<dc:description xml:lang="en-US">Automated machine learning is a promising approach widely used to solve classification and prediction problems, which currently receives much attention for modification and improvement. One of the progressing works for automated machine learning improvement is the inclusion of evolutionary algorithm such as Genetic Programming. The function of Genetic Programming is to optimize the best combination of solutions from the possible pipelines of machine learning modelling, including selection of algorithms and parameters optimization of the selected algorithm.  As a family of evolutionary based algorithm, the effectiveness of Genetic Programming in providing the best machine learning pipelines for a given problem or dataset is substantially depending on the algorithm parameterizations including the mutation and crossover rates. This paper presents the effect of different pairs of mutation and crossover rates on the automated machine learning performances that tested on different types of datasets. The finding can be used to support the theory that higher crossover rates used to improve the algorithm accuracy score while lower crossover rates may cause the algorithm to converge at earlier stage.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2020-09-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/20499</dc:identifier>
	<dc:identifier>10.11591/ijai.v9.i3.pp402-408</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 9, No 3: September 2020; 402-408</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v9.i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/20499/12965</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2020 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>