Collaborative Learning Team Formation Considering Team Roles: An Evolutionary Approach based on Adaptive Crossover, Mutation and Simulated Annealing

In this paper, a hybrid evolutionary algorithm is proposed to solve a collaborative learning team formation problem in higher education contexts. This problem involves a grouping criterion evaluated satisfactorily in a great variety of higher education courses as well as training programs. This criterion is based on the team roles of students, and implies forming well-balanced teams respecting the team roles of their members. The hybrid evolutionary algorithm uses adaptive crossover, mutation and simulated annealing processes, in order to improve the performance of the evolutionary search. These processes adapt their behavior regarding the state of the evolutionary search. The performance of the hybrid evolutionary algorithm is exhaustively evaluated on data sets with very different complexity levels, and after that, is compared with those of the algorithms previously reported in the literature to solve the addressed problem. The results obtained from the performance comparison indicate that the hybrid evolutionary algorithm significantly outperforms the algorithms previously reported, in both effectiveness and efficiency.Fil: Yannibelli, Virginia Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; ArgentinaFil: Amandi, Analia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; Argentin

Enfoque evolutivo basado en conocimiento para planeamiento de proyectos

El planeamiento de proyectos es una tarea central, no trivial y costosa para muchas organizaciones. En este artículo, se propone un enfoque evolutivo basado en conocimiento para asistir a los administradores de proyectos en la fase temprana del planeamiento. El enfoque diseña automáticamente planes válidos para un proyecto dado, y evalúa cada plan en relación a un objetivo de optimización que es prioritario para los administradores en la fase temprana. Dicho objetivo consiste en asignar a cada actividad del proyecto el equipo de empleados más efectivo para llevarla a cabo. Por este motivo, la evaluación de los planes es desarrollada en base a conocimiento histórico disponible sobre la efectividad de los empleados involucrados. El enfoque evolutivo propuesto es evaluado sobre ocho conjuntos de problemas de planeamiento. Los resultados obtenidos son prometedores ya que el enfoque ha logrado un nivel óptimo de efectividad sobre siete de los ocho conjuntos mencionados.Sociedad Argentina de Informática e Investigación Operativ

A comparative analysis of NSGA-II and NSGA-III for autoscaling parameter sweep experiments in the cloud

The Cloud Computing paradigm is focused on the provisioning of reliable and scalable virtual infrastructures that deliver execution and storage services. This paradigm is particularly suitable to solve resource-greedy scientific computing applications such as parameter sweep experiments (PSEs). Through the implementation of autoscalers, the virtual infrastructure can be scaled up and down by acquiring or terminating instances of virtual machines (VMs) at the time that application tasks are being scheduled. In this paper, we extend an existing study centered in a state-of-the-art autoscaler called multiobjective evolutionary autoscaler (MOEA). MOEA uses a multiobjective optimization algorithm to determine the set of possible virtual infrastructure settings. In this context, the performance of MOEA is greatly influenced by the underlying optimization algorithm used and its tuning. Therefore, we analyze two well-known multiobjective evolutionary algorithms (NSGA-II and NSGA-III) and how they impact on the performance of the MOEA autoscaler. Simulated experiments with three real-world PSEs show that MOEA gets significantly improved when using NSGA-III instead of NSGA-II due to the former provides a better exploitation versus exploration trade-off.Fil: Yannibelli, Virginia Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Pacini Naumovich, Elina Rocío. Universidad Nacional de Cuyo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Monge, David. Universidad Nacional de Cuyo; ArgentinaFil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Rodríguez, Guillermo Horacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

An In-depth Benchmarking of Evolutionary and Swarm Intelligence Algorithms for Autoscaling Parameter Sweep Applications on Public Clouds

Many important computational applications in science, engineering, industry, and technology are represented by PSE (parameter sweep experiment) applications. Tese applications involve a large number of resource-intensive and independent computational tasks. Because of this, cloud autoscaling approaches have been proposed to execute PSE applications on public cloud environments that ofer instances of diferent VM (virtual machine) types, under a pay-per-use scheme, to execute diverse applications. One of the most recent approaches is the autoscaler MOEA (multiobjective evolutive algorithm), which is based on the multiobjective evolutionary algorithm NSGA-II (nondominated sorting genetic algorithm II). MOEA considers on-demand and spot VM instances and three optimization objectives relevant for users: minimizing the computing time, monetary cost, and spot instance interruptions of the application’s execution. However, MOEA’s performance regarding these optimization objectives depends signifcantly on the optimization algorithm used. It has been shown recently that MOEA’s performance improves considerably when NSGA-II is replaced by a more recent algorithm named NSGA-III. In this paper, we analyze the incorporation of other multiobjective optimization algorithms into MOEA to enhance the performance of this autoscaler. First, we consider three multiobjective optimization algorithms named E-NSGA-III (extreme NSGA-III), SMS-EMOA (S-metric selection evolutionary multiobjective optimization algorithm), and SMPSO (speed-constrained multiobjective particle swarm optimization), which have behavioral diferences with NSGA-III. Ten, we evaluate the performance of MOEA with each of these algorithms, considering the three optimization objectives, on four real-world PSE applications from the meteorology and molecular dynamics areas, considering diferent application sizes. To do that, we use the well-known CloudSim simulator and consider diferent VM types available in Amazon EC2. Finally, we analyze the obtained performance results, which show that MOEA with E-NSGA-III arises as the best alternative, reaching better and signifcant savings in terms of computing time (10%–17%), monetary cost (10%– 40%), and spot instance interruptions (33%–100%).Fil: Yannibelli, Virginia Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Pacini Naumovich, Elina Rocío. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Monge Bosdari, David Antonio. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Rodríguez, Guillermo Horacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Millán, Emmanuel Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Santos, Jorge Ruben. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentin

A hybrid evolutionary algorithm based on adaptive mutation and crossover for collaborative learning team formation in higher education

In this paper, we address a collaborative learning team formation problem in higher education environments. This problem considers a grouping criterion successfully evaluated in a wide variety of higher education courses and training programs. To solve the problem, we propose a hybrid evolutionary algorithm based on adaptive mutation and crossover processes. The behavior of these processes is adaptive according to the diversity of the evolutionary algorithm population. These processes are meant to enhance the evolutionary search. The performance of the hybrid evolutionary algorithm is evaluated on ten different data sets, and then, is compared with that of the best algorithm previously proposed in the literature for the addressed problem. The obtained results indicate that the hybrid evolutionary algorithm considerably outperforms the previous algorithm.Fil: Yannibelli, Virginia Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; ArgentinaFil: Amandi, Analia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; Argentin

A knowledge-based evolutionary assistant to software development project scheduling

The scheduling of software development projects is a central, non-trivial and costly task for software companies. This task is not exempt of erroneous decisions caused by human limitations inherent to project managers. In this paper, we propose a knowledge-based evolutionary approach with the aim of assisting to project managers at the early stage of scheduling software projects. Given a software project to be scheduled, the approach automatically designs feasible schedules for the project, and evaluates each designed schedule according to an optimization objective that is priority for managers at the mentioned stage. Our objective is to assign the most effective set of employees to each project activity. For this reason, the evaluation of designed schedules in our approach is developed based on available knowledge about the competence of the employees involved in each schedule. This knowledge arises from historical information about the participation of the employees in already executed projects. In order to evaluate the performance of our evolutionary approach, we present computational experiments developed over eight different sets of problem instances. The obtained results are promising since this approach has reached an optimal level of effectivity on seven of the eight mentioned sets, and a high level of effectivity on the remaining set.Fil: Yannibelli, Virginia Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Amandi, Analia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

Hybridizing a multi-objective simulated annealing algorithm with a multi-objective evolutionary algorithm to solve a multi-objective project scheduling problem

In this paper, a multi-objective project scheduling problem is addressed. This problem considers two conflicting, priority optimization objectives for project managers. One of these objectives is to minimize the project makespan. The other objective is to assign the most effective set of human resources to each project activity. To solve the problem, a multi-objective hybrid search and optimization algorithm is proposed. This algorithm is composed by a multi-objective simulated annealing algorithm and a multi-objective evolutionary algorithm. The multi-objective simulated annealing algorithm is integrated into the multi-objective evolutionary algorithm to improve the performance of the evolutionary-based search. To achieve this, the behavior of the multi-objective simulated annealing algorithm is self-adaptive to either an exploitation process or an exploration process depending on the state of the evolutionary-based search. The multi-objective hybrid algorithm generates a number of near non-dominated solutions so as to provide solutions with different trade-offs between the optimization objectives to project managers. The performance of the multi-objective hybrid algorithm is evaluated on nine different instance sets, and is compared with that of the only multi-objective algorithm previously proposed in the literature for solving the addressed problem. The performance comparison shows that the multi-objective hybrid algorithm significantly outperforms the previous multi-objective algorithm.Fil: Yannibelli, Virginia Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Amandi, Analia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

Hybrid Evolutionary Algorithm with Adaptive Crossover, Mutation and Simulated Annealing Processes to Project Scheduling

In this paper, we address a project scheduling problem that considers a priority optimization objective for project managers. This objective involves assigning the most effective set of human resources to each project activity. To solve the problem, we propose a hybrid evolutionary algorithm. This algorithm uses adaptive crossover, mutation and simulated annealing processes in order to improve the performance of the evolutionary search. These processes adapt their behavior based on the diversity of the evolutionary algorithm population. We compare the performance of the hybrid evolutionary algorithm with those of the algorithms previously proposed in the literature for solving the addressed problem. The obtained results indicate that the hybrid evolutionary algorithm significantly outperforms the previous algorithms.Fil: Yannibelli, Virginia Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Amandi, Analia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentin

Project scheduling: A multi-objective evolutionary algorithm that optimizes the effectiveness of human resources and the project makespan

In this article, the project scheduling problem is addressed in order to assist project managers at the early stage of scheduling. Thus, as part of the problem, two priority optimization objectives for managers at that stage are considered. One of these objectives is to assign the most effective set of human resources to each project activity. The effectiveness of a human resource is considered to depend on its work context. The other objective is to minimize the project makespan. To solve the problem, a multi-objective evolutionary algorithm is proposed. This algorithm designs feasible schedules for a given project and evaluates the designed schedules in relation to each objective. The algorithm generates an approximation to the Pareto set as a solution to the problem. The computational experiments carried out on nine different instance sets are reported.Fil: Yannibelli, Virginia Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Amandi, Analia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

A deterministic crowding evolutionary algorithm to form learning teams in a collaborative learning context

The aim of forming collaborative learning teams is that participating students acquire new knowledge and skills through the interaction with their peers. To reach this aim, teachers usually utilize a grouping criterion based on the students' roles and on forming well-balanced teams according to the roles of their members. However, the implementation of this criterion requires a considerable amount of time, effort and knowledge on the part of the teachers. In this paper, we propose a deterministic crowding evolutionary algorithm with the aim of assisting teachers when forming well-balanced collaborative learning teams. Considering a given number of students who must be divided into a given number of teams, the algorithm both designs different alternatives to divide students into teams and evaluates each alternative as regards the grouping criterion previously mentioned. This evaluation is carried out on the basis of knowledge of the students' roles. To analyze the performance of the proposed algorithm, we present the computational experiments developed on ten data sets with different levels of complexity. The obtained results are really promising since the algorithm has reached optimal solutions for the first four data sets and near-optimal solutions for the remaining six data sets.Fil: Yannibelli, Virginia Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Amandi, Analia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin