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.

A parallel bacterial foraging algorithm was developed for the multiple sequence alignment problem. Four sets of homologous genetic and protein sequences related to Alzheimer’s disease among various species were collected from the NCBI database for convergence analysis and performance comparison. The main question was the following: is the bacterial foraging algorithm suitable for the multiple sequence alignment problem? Three versions of the algorithm were contrasted by performing a t-test and Mann–Whitney test based on the results of a 30-run scheme, focusing on fitness, execution time, and the number of function evaluations as performance metrics. Additionally, we conducted a performance comparison of the developed algorithm with the well-known Genetic Algorithm. The results demonstrated the consistent efficiency of the bacterial foraging algorithm, while the version of the algorithm based on gap deletion presented an increased number of function evaluations and excessive execution time. Overall, the first version of the developed algorithm was found to outperform the second version, based on its efficiency. Finally, we found that the third bacterial foraging algorithm version outperformed the Genetic Algorithm in the third phase of the experiment. The sequence sets, the algorithm’s Python 3.12 code and pseudocode, the data collected from the executions, and a GIF animation of the convergence on various different sets are available for download.

El objetivo de este trabajo es desarrollar un algoritmo de forrajeo de bacterias para el alineamiento múltiple de secuencias genéticas. La metodología consiste en comparar el desempeño de dos versiones del algoritmo (bactOf y bactAtp) en el alineamiento de tres conjuntos de secuencias de genes (A,B y C) relacionados con el AlzHeimer. Dichos genes son TREM2, EPHA1 y ABCA7 de diferentes organismos como Homo sapiens, Mustela putorius furo, Pongo abelii y Cervus elaphus, entre otros. Los resultados muestran que en el set A, el promedio de fitness de bactOf comienza en 1097.86 y aumenta gradualmente hasta alcanzar 1431.87 en la iteración 30, mientras que bactAtp inicia en 1570.53 y aumenta gradualmente hasta estabilizarse en 1596.10 a partir de la iteración 20. En el set B, bactAtp comienza con un promedio de fitness de 5636.59, mientras que bactOf inicia en 3626.53, lo que indica que bactAtp tiene un valor de fitness inicial significativamente superior. Finalmente, en el set C, bactAtp comienza con un valor de fitness mucho más alto (3239.54) que bactOf (492.07). Esta tendencia de mayor fitness en bactAtp se mantiene constante a lo largo de las iteraciones. Por otro lado, la cuantificación del número de funciones evaluadas (NFE) indica que en los Sets A y B, ambos algoritmos presentan un NFE idéntico de 653. Conclusiones: aunque ambos algoritmos, bactOf y bactAtp, son comparables en eficiencia de evaluación, bactAtp muestra un mejor desempeño en términos de fitness y rapidez de convergencia; esto es más notorio en el Set B. Esto se debe a su distribución aleatoria de gaps que optimiza la diversidad genética y evita patrones no deseados, lo que permite una mejor exploración y evita mínimos locales.