TY  - JOUR
AV  - none
SP  - 402
TI  - Different mutation and crossover set of genetic programming in an automated machine learning
N1  - cited By 5
SN  - 20894872
PB  - Institute of Advanced Engineering and Science
EP  - 408
ID  - scholars12795
N2  - 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.
IS  - 3
Y1  - 2020///
VL  - 9
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087002616&doi=10.11591%2fijai.v9.i3.pp402-408&partnerID=40&md5=7773b7578076b832eb9de31679f27622
JF  - IAES International Journal of Artificial Intelligence
A1  - Masrom, S.
A1  - Mohamad, M.
A1  - Hatim, S.M.
A1  - Baharun, N.
A1  - Omar, N.
A1  - Abd. Rahman, A.S.
ER  -