Construction of Computer Algorithms in Bioinformatics of the Fusion Genetic Algorithm

With the continuous in-depth exploration of life sciences, bioinformatics based on life sciences, computer algorithms, and statistics have gradually developed. The research of bioinformatics mainly focuses on the study of genes, and the structural characteristics of genes lead to a large amount of e...

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Bibliographic Details
Published in:Mathematical problems in engineering 2022-09, Vol.2022, p.1-8
Main Author: Sun, Haimeng
Format: Article
Language:English
Subjects:
Accuracy
Alignment
Ant colony optimization
Bioinformatics
Biological computing
Convergence
Dynamic programming
Efficiency
Engineering
Gene sequencing
Genes
Genetic algorithms
Interdisciplinary aspects
Life sciences
Optimization
Pheromones
Science
Sensitivity analysis
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Summary:With the continuous in-depth exploration of life sciences, bioinformatics based on life sciences, computer algorithms, and statistics have gradually developed. The research of bioinformatics mainly focuses on the study of genes, and the structural characteristics of genes lead to a large amount of extremely complex data in the study of bioinformatics. Analyzing data in bioinformatics research requires accurate calculation by computer algorithms. However, common computer algorithms such as the dynamic programming algorithm and the genetic algorithm have the disadvantages of large memory or inaccurate optimization. Combining the ant colony algorithm (ACA) and GA can give the advantages of the two methods that should be fully utilized to efficiently analyze the biological information data. In this paper, the ant colony fusion genetic algorithm (ACA-GA), GA, and dynamic programming algorithm are used to compare and analyze the sensitivity, convergence speed, sequence alignment accuracy, and required memory space of gene sequences. The experimental results show that compared with ACA-GA, the dynamic programming algorithm has the advantage of finding the optimal alignment of 100%, but the memory required is too large, the memory required is more than 10 times that of the ant colony fusion genetic algorithm and the sensitivity is not as good as ACA-GA. The convergence speed of ACA-GA is faster than the gene comparison speed of GA and the accuracy is 2.6% better than that of GA on average. ACA-GA has the advantages of GA and ACA, which can improve the computational efficiency of biological data in bioinformatics.
ISSN:1024-123X
1563-5147
DOI:10.1155/2022/8632490