A Modified Memetic Algorithm with an Application to Gene Selection in a Sheep Body Weight Study
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,...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Animals |
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| Online Access: | https://www.mdpi.com/2076-2615/12/2/201 |
| _version_ | 1797496346967539712 |
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| author | Maoxuan Miao Jinran Wu Fengjing Cai You-Gan Wang |
| author_facet | Maoxuan Miao Jinran Wu Fengjing Cai You-Gan Wang |
| author_sort | Maoxuan Miao |
| collection | DOAJ |
| description | 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 <i>add</i> and <i>del</i> 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. |
| first_indexed | 2024-03-10T03:02:22Z |
| format | Article |
| id | doaj.art-0c56878e62e2414395206126abd17ab4 |
| institution | Directory Open Access Journal |
| issn | 2076-2615 |
| language | English |
| last_indexed | 2024-03-10T03:02:22Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Animals |
| spelling | doaj.art-0c56878e62e2414395206126abd17ab42023-11-23T12:42:09ZengMDPI AGAnimals2076-26152022-01-0112220110.3390/ani12020201A Modified Memetic Algorithm with an Application to Gene Selection in a Sheep Body Weight StudyMaoxuan Miao0Jinran Wu1Fengjing Cai2You-Gan Wang3College of Mathematics and Physics, Wenzhou University, Wenzhou 325035, ChinaSchool of Mathematical Sciences, Queensland University of Technology, Brisbane 4001, AustraliaCollege of Mathematics and Physics, Wenzhou University, Wenzhou 325035, ChinaSchool of Mathematical Sciences, Queensland University of Technology, Brisbane 4001, AustraliaSelecting 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 <i>add</i> and <i>del</i> 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.https://www.mdpi.com/2076-2615/12/2/201gene selectionsheep weightmemetic algorithmmodificationslocal search operator |
| spellingShingle | Maoxuan Miao Jinran Wu Fengjing Cai You-Gan Wang A Modified Memetic Algorithm with an Application to Gene Selection in a Sheep Body Weight Study Animals gene selection sheep weight memetic algorithm modifications local search operator |
| title | A Modified Memetic Algorithm with an Application to Gene Selection in a Sheep Body Weight Study |
| title_full | A Modified Memetic Algorithm with an Application to Gene Selection in a Sheep Body Weight Study |
| title_fullStr | A Modified Memetic Algorithm with an Application to Gene Selection in a Sheep Body Weight Study |
| title_full_unstemmed | A Modified Memetic Algorithm with an Application to Gene Selection in a Sheep Body Weight Study |
| title_short | A Modified Memetic Algorithm with an Application to Gene Selection in a Sheep Body Weight Study |
| title_sort | modified memetic algorithm with an application to gene selection in a sheep body weight study |
| topic | gene selection sheep weight memetic algorithm modifications local search operator |
| url | https://www.mdpi.com/2076-2615/12/2/201 |
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