A modified memetic algorithm with an application to gene selection in a sheep body weight study

Journal article


Miao, Maoxuan, Wu, Jinran, Cai, Fengjing and Wang, You-Gan. (2022). A modified memetic algorithm with an application to gene selection in a sheep body weight study. Animals. 12(2), p. Article 201. https://doi.org/10.3390/ani12020201
AuthorsMiao, Maoxuan, Wu, Jinran, Cai, Fengjing and Wang, You-Gan
Abstract

Selecting the minimal best subset out of a huge number of factors for influencing the response is a fundamental and very challenging NP-hard problem because the presence of many redundant genes results in over-fitting easily while missing an important gene can more detrimental impact on predictions, and computation is prohibitive for exhaust search. We propose a modified memetic algorithm (MA) based on an improved splicing method to overcome the problems in the traditional genetic algorithm exploitation capability and dimension reduction in the predictor variables. The new algorithm accelerates the search in identifying the minimal best subset of genes by incorporating it into the new local search operator and hence improving the splicing method. The improvement is also due to another two novel aspects: (a) updating subsets of genes iteratively until the no more reduction in the loss function by splicing and increasing the probability of selecting the true subsets of genes; and (b) introducing add and del operators based on backward sacrifice into the splicing method to limit the size of gene subsets. Additionally, according to the experimental results, our proposed optimizer can obtain a better minimal subset of genes with a few iterations, compared with all considered algorithms. Moreover, the mutation operator is replaced by it to enhance exploitation capability and initial individuals are improved by it to enhance efficiency of search. A dataset of the body weight of Hu sheep was used to evaluate the superiority of the modified MA against the genetic algorithm. According to our experimental results, our proposed optimizer can obtain a better minimal subset of genes with a few iterations, compared with all considered algorithms including the most advanced adaptive best-subset selection algorithm.

Keywordsgene selection; sheep weight; memetic algorithm; modifications; local search operator
Year2022
JournalAnimals
Journal citation12 (2), p. Article 201
PublisherMultidisciplinary Digital Publishing Institute (MDPI AG)
ISSN2076-2615
Digital Object Identifier (DOI)https://doi.org/10.3390/ani12020201
PubMed ID35049823
Scopus EID2-s2.0-85122872154
PubMed Central IDPMC8772977
Open accessPublished as ‘gold’ (paid) open access
Page range1-11
FunderAustralian Research Council (ARC)
Zhejiang Provincial Natural Science Foundation of China
Publisher's version
License
File Access Level
Open
Output statusPublished
Publication dates
Online15 Jan 2022
Publication process dates
Accepted14 Jan 2022
Deposited07 Oct 2022
ARC Funded ResearchThis output has been funded, wholly or partially, under the Australian Research Council Act 2001
Grant IDDP160104292
CE140100049
LY19A010014
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