eprintid: 12795
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/27/95
datestamp: 2023-11-10 03:27:21
lastmod: 2023-11-10 03:27:21
status_changed: 2023-11-10 01:49:32
type: article
metadata_visibility: show
creators_name: Masrom, S.
creators_name: Mohamad, M.
creators_name: Hatim, S.M.
creators_name: Baharun, N.
creators_name: Omar, N.
creators_name: Abd. Rahman, A.S.
title: Different mutation and crossover set of genetic programming in an automated machine learning
ispublished: pub
note: cited By 5
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. © 2020, Institute of Advanced Engineering and Science. All rights reserved.
date: 2020
publisher: Institute of Advanced Engineering and Science
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087002616&doi=10.11591%2fijai.v9.i3.pp402-408&partnerID=40&md5=7773b7578076b832eb9de31679f27622
id_number: 10.11591/ijai.v9.i3.pp402-408
full_text_status: none
publication: IAES International Journal of Artificial Intelligence
volume: 9
number: 3
pagerange: 402-408
refereed: TRUE
issn: 20894872
citation:   Masrom, S. and Mohamad, M. and Hatim, S.M. and Baharun, N. and Omar, N. and Abd. Rahman, A.S.  (2020) Different mutation and crossover set of genetic programming in an automated machine learning.  IAES International Journal of Artificial Intelligence, 9 (3).  pp. 402-408.  ISSN 20894872