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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) · hlm. 402–408

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

Abstrak

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.

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  • Universiti Teknologi MARA
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Rekan penulisOrganisasiArtikel bersama
Rahayu Abdul RahmanAfiliasi tidak diketahui4
Thuraiya MohdAfiliasi tidak diketahui3
Teoh Sian HoonAfiliasi tidak diketahui3
Nadirah OthmanAfiliasi tidak diketahui3
Nor Aslily SarkamAfiliasi tidak diketahui3
Nor Faezah Mohamad RaziAfiliasi tidak diketahui3
Nor Faezah Mohamad RaziAfiliasi tidak diketahui2
Mohamed Imran Mohamed AriffUniversiti Teknologi MARA1
Samsiah Binti AhmadUniversiti Teknologi MARA1
Mohammad AbdullahUniversiti Teknologi MARA1
Nur Anim Abdul HalimAfiliasi tidak diketahui1
Anis Zafirah AzmiUniversiti Teknologi MARA1
Siti Khatijah Nor Abdul RahimAfiliasi tidak diketahui1
Raseeda HamzahAfiliasi tidak diketahui1
Megat Nabil Irwan Megat AmerudinAfiliasi tidak diketahui1
Mohd Suffian SulaimanAfiliasi tidak diketahui1
Amir Hamzah JaafarAfiliasi tidak diketahui1
Anis Zulaikha Mohd ZukriAfiliasi tidak diketahui1
Siti Rasidah Md SakipAfiliasi tidak diketahui1
Syafiqah JamilAfiliasi tidak diketahui1
Maheran ZakariaAfiliasi tidak diketahui1
Rahayu Abdul RahmanAfiliasi tidak diketahui1
Normah OmarAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<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>