La optimización evolutiva multi objetivo en la confección de equipos de desarrollo de software: una forma de lograr la calidad en el producto final

Peña, Yasnalla Rivero; Hernández, Pavel Novoa; Ochoa, Yandi Fernández

doi:10.29019/enfoqueute.v6n1.53

Cited by 3 publications

(2 citation statements)

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“…They are based on the genetic process of living organisms. Genetic algorithms use heuristics for problem solving, which drastically limits the number of data to be used (Najarro, López, Racines, & Puris, 2017) (Rivero Peña, Novoa Hernández, & Fernández Ochoa, 2015. Algorithms with heuristic meta-heuristics are used to achieve optimal values in various areas of knowledge; in Viera Carcache, Vinces Llaguno, Guerrero Goyes, & Marcillo Vera (2017) the heuristic meta-heuristic Optimization based on Ant Cologne (ACO) is used for a mathematical model in applications in mobile phone systems.…”

Section: Introductionmentioning

confidence: 99%

Parametric Analysis of BFOA for Minimization Problems Using a Benchmark Function

et al. 2019

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This paper presents the social foraging behavior of Escherichia coli (E. Coli) bacteria based on Bacteria Foraging Optimization algorithms (BFOA) to find optimization and distributed control values. The search strategy for E. coli is very complex to express and the dynamics of the simulated chemotaxis stage in BFOA is analyzed with the help of a simple mathematical model. The methodology starts from a detailed analysis of the parameters of bacterial swimming and tumbling (C) and the probability of elimination and dispersion (Ped), then an adaptive variant of BFOA is proposed, in which the size of the chemotherapeutic step is adjusted according to the current suitability of a virtual bacterium. To evaluate the performance of the algorithm in obtaining optimal values, the resolution was applied to one of the benchmark functions, in this case the Ackley minimization function, a comparative analysis of the BFOA is then performed. The simulation results have shown the validity of the optimal values (minimum or maximum) obtained on a specific function for real world problems, with a function belonging to the benchmark group of optimization functions.

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Section: Introductionmentioning

confidence: 99%

Parametric Analysis of BFOA for Minimization Problems Using a Benchmark Function

et al. 2019

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“…Esto es, se asume que el docente es quién debe realizar esta tarea de forma manual. Por otro lado, existen trabajos que automatizan procesos de creación de equipos en el contexto de: la educación superior (Novoa-Hernández, NovoaHernández, & Rivero-Peña, 2013;Novoa-Hernández, 2015), el desarrollo de software (Escalera Fariñas, Infante Abreu, André Ampuero, & Rosete Suárez, 2014;Rivero Peña, Novoa-Hernández, & Fernández Ochoa, 2015), y los deportes (Ahmed, Jindal, & Deb, 2011). En todos estos trabajos se aplican técnicas computacionales que resuelven eficientemente problemas complejos de creación de equipos (Wegener, 2005).…”

Section: Introductionunclassified

Creación automática de equipos de estudiantes universitarios: una experiencia desde la asignatura Inglés / Automatic Building of University Student Teams: an experience from English subject

Gavidia

Galarza

Robalino

et al. 2017

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Creación automática de equipos de estudiantes universitarios: una experiencia desde la asignatura InglésAutomatic Building of University Student Teams: an experience from English subject ResumenUno de los principales objetivos en la educación es lograr que los estudiantes desarrollen la capacidad de trabajo en equipo. Esta capacidad potencia la socialización entre los estudiantes y la resolución de problemas complejos. Comúnmente, la creación de estos equipos es realizada por el docente de la asignatura, quien debe tener en cuenta múltiples criterios como la presencia de un estudiante líder y equipos heterogéneos. Este proceso se torna complejo cuando existen muchos estudiantes, y por lo general no existe garantía de que los equipos creados cumplan con los criterios deseados. En este sentido, la presente investigación propone una solución computacional que automatiza la creación de equipos de trabajo de estudiantes. Específicamente, la tarea de la creación de los equipos se modeló matemáticamente como un problema de optimización de tipo combinatorio y multi-objetivo, que fue resuelto a su vez por un algoritmo evolutivo basado en los conceptos de Dominancia de Pareto. Para validar las propuestas, se realizaron varios experimentos computacionales que involucran escenarios reales, relacionados con la asignatura Inglés en carreras de la Universidad Técnica Estatal de Quevedo. Los resultados indican que la propuesta es capaz de crear equipos balanceados de acuerdo a los criterios considerados.Palabras Clave: creación de equipos; educación superior; idioma Inglés; optimización multi-objetivo. AbstractOne of the main goals for Higher Education is to educate students to work in teams. Such a skill not only improves their social behavior in the community, but also the ability for solving complex problems. Usually, the process of making teams is carried out by professor of the subject, who has to take into account several criteria (e.g. the presence of leader, heterogeneity of the team according the level of knowledge, sex, among others). When the subject has just few students, this task becomes easy. However, in the case of classes with a large number of students, this task becomes complex and there is no warranty about the accomplishment of the considered criteria. In that sense, the present work proposes a computational solution that automatizes the task of student teams building. Specifically, it was approached as a multi-objective combinatorial optimization problem, which was solved using a Pareto Dominance-based algorithm. In order to validate the proposal we performed several computational experiments involving real case studies from the English subject of three careers at the Technical State University of Quevedo. Results show that the proposed approach is able to build balanced teams according to the considered criteria.

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Players’ selection for basketball teams, through Performance Index Rating, using multiobjective evolutionary algorithms

et al. 2019

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In any sport the selection of players for a team is fundamental for its subsequent performance. Many factors condition the selection process from the characteristics of the sport discipline to financial limitations, including a long list of restrictions associated with the environment of the competitions in which the team takes part. All of this makes the process of selecting a roster of players very complex, as it is affected by multiple variables and in many cases marked by a great deal of subjectivity. The purpose of this article was to objectively select the players for a basketball team using an evolutionary algorithm, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) that uses stochastic search methods based on the imitation of natural biological evolution. The sample was composed of the players from the teams competing in the top Spanish basketball league, the Association of Basketball Clubs (ACB). To assess the quality of the solutions obtained, the results were compared with the teams in the ACB playing in the same competition as the players used in the study. The results make it possible to obtain different solutions for composing teams rendering financial resources profitable and taking into account the restrictions of the competition and of each sport management.

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La optimización evolutiva multi objetivo en la confección de equipos de desarrollo de software: una forma de lograr la calidad en el producto final

Cited by 3 publications

References 4 publications

Parametric Analysis of BFOA for Minimization Problems Using a Benchmark Function

Parametric Analysis of BFOA for Minimization Problems Using a Benchmark Function

Creación automática de equipos de estudiantes universitarios: una experiencia desde la asignatura Inglés / Automatic Building of University Student Teams: an experience from English subject

Players’ selection for basketball teams, through Performance Index Rating, using multiobjective evolutionary algorithms

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