Evolutionary Computation in bioinformatics: A survey

• Published in: Neurocomputing (Amsterdam) Vol. 591; p. 127758
• Main Authors: Zhang, Yanyun, Cheng, Li, Chen, Guanyu, Alghazzawi, Daniyal
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 28.07.2024
• Subjects: Bioinformatics; Evolutionary algorithm; Evolutionary Computation; Multi-objectives optimization; Single-objective optimization; Multi-objectives optimization; Evolutionary Computation; Bioinformatics; Evolutionary algorithm; Single-objective optimization
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2024.127758

Bioinformatics is a subject that studies life phenomena by using mathematical and information science theories and techniques. Its main tasks, such as DNA sequence comparison, protein structure prediction and cell metabolism analysis, can be regarded as complex optimization problems with different characteristics. Evolutionary computing is a kind of global optimization algorithm inspired by nature. Over the years, scholars have accumulated fruitful results in solving complex optimization problems such as large-scale, dynamic, multi-modal, multi-objective and multi-constrained problems by using Evolutionary Computation algorithms, and have successfully applied to the above optimization tasks in bioinformatics. This paper mainly summarizes the work of Evolutionary Computation technologies in bioinformatics from 2019 to 2023 at multiple levels, including Genomics, Proteomics, metabolomics and molecular networks related optimization tasks, as well as further applications in disease diagnosis and drug development. •Recent works on Evolutionary Computation technologies in Bioinformatics from 2019 to 2023.•Optimization tasks in Bioinformatics from genotype to phenotype.•Evolutionary Computation technologies from Genomics, Proteomics, Metabolomics and Molecular Networks level respectively.•Downstream applications such as disease diagnosis and drug discovery.