A multitasking multi-objective differential evolution gene selection algorithm enhanced with new elite and guidance strategies for tumor identification

•MMODE is developed as a new hybrid gene selection method for tumor identification.•MMODE combines multi-tasking and multi-objective frameworks.•MMODE uses a new elite strategy and a new guidance strategy.•MMODE selects a few genes and achieves high classification accuracy. A key preprocessing step...

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Bibliographic Details
Published in:Expert systems with applications Vol. 241; p. 122701
Main Authors: Li, Min, Zhao, Yangfan, Lou, Mingzhu, Deng, Shaobo, Wang, Lei
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.05.2024
Subjects:
Evolution multitasks
Feature selection
Gene selection
Microarray expression profile data
Multi-objection differential evolution
Feature selection
Gene selection
Multi-objection differential evolution
Microarray expression profile data
Evolution multitasks
ISSN:0957-4174, 1873-6793
Online Access:Get full text
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Summary:•MMODE is developed as a new hybrid gene selection method for tumor identification.•MMODE combines multi-tasking and multi-objective frameworks.•MMODE uses a new elite strategy and a new guidance strategy.•MMODE selects a few genes and achieves high classification accuracy. A key preprocessing step in tumor recognition based on microarray expression profile data and machine learning is to identify tumor marker genes. Gene selection aims to select the most relevant gene subset from the original ultra-high dimensional microarray expression profile data to improve tumor identification performance. Inspired by evolutionary multitasking (EMT) and multi-objective optimization, this paper puts forward a novel multitasking multi-objective differential evolution gene selection algorithm (MMODE) which uses new elite and guidance strategies to select the best gene subsets. MMODE initializes two different populations according to different filtering criteria to increase the diversity of the search. These two populations guide their respective populations to search in the optimal direction through knowledge transfer in the evolutionary process. In addition, MMODE employs new elite and guidance strategies that enables individuals to narrow the search range and jump out of local optima. The proposed algorithm is validated on 13 publicly available microarray expression datasets in comparison with state-of-the-art gene selection algorithms. The experimental results show that MMODE can find smaller gene subsets and achieve higher classification accuracy compared with other algorithms.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2023.122701