A subspace inertial method for derivative-free nonlinear monotone equations

Kimiaei, Morteza; Hassan Ibrahim, Abdulkarim; Ghaderi, Susan

doi:10.1080/02331934.2023.2252849

Cited by 10 publications

(1 citation statement)

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“…In the field of genetics, the parallelization strategy of processing information has shown significant improvement in the results, especially in reducing the computational time required to solve various problems [10] [11] [12] . As a validation method, the numerical performance profile measures the relative efficiency of the algorithm in solving the set of problems [13] . Bioinformatics software testing is often complex due to the difficulty in verifying the correctness of output and effectively generating failure-revealing test cases [14] .…”

Section: Introductionmentioning

confidence: 99%

Enhancing Multiple Sequence Alignment with Genetic Algorithms: A Bioinformatics Approach in Biomedical Engineering

Rios-Willars,

Velez-Segura,

Delabra-Salinas

2024

Rev. Mex. Ing. Biomed.

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This study aimed to create a genetic information processing technique for the problem of multiple alignment of genetic sequences in bioinformatics. The objective was to take advantage of the computer hardware's capabilities and analyze the results obtained regarding quality, processing time, and the number of evaluated functions. The methodology was based on developing a genetic algorithm in Java, which resulted in four different versions: Gp1, Gp2, Gp3 and Gp4 . A set of genetic sequences were processed, and the results were evaluated by analyzing numerical behavior profiles. The research found that algorithms that maintained diversity in the population produced better quality solutions, and parallel processing reduced processing time. It was observed that the time required to perform the process decreased, according to the generated performance profile. The study concluded that conventional computer equipment can produce excellent results when processing genetic information if algorithms are optimized to exploit hardware resources. The computational effort of the hardware used is directly related to the number of evaluated functions. Additionally, the comparison method based on the determination of the performance profile is highlighted as a strategy for comparing the algorithm results in different metrics of interest, which can guide the development of more efficient genetic information processing techniques.

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