A Diversity-Adaptive Hybrid Evolutionary Algorithm to Solve a Project Scheduling Problem

In this paper, we address a project scheduling problem. This problem considers a priority optimization objective for project managers. This objective implies assigning the most effective set of human resources to each project activity. To solve the problem, we propose a hybrid evolutionary algorithm. This algorithm incorporates a diversity-adaptive simulated annealing algorithm into the framework of an evolutionary algorithm with the aim of improving the performance of the evolutionary search. The simulated annealing algorithm adapts its behavior according to the fluctuation of diversity of evolutionary algorithm population. The performance of the hybrid evolutionary algorithm on six different instance sets is compared with those of the algorithms previously proposed in the literature for solving the addressed problem. The obtained results show that the hybrid evolutionary algorithm significantly outperforms the previous algorithms.Fil: Amandi, Analia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Yannibelli, Virginia Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentin

Scheduling Projects by a Hybrid Evolutionary Algorithm with Self-Adaptive Processes

In this paper, we present a hybrid evolutionary algorithm with self-adaptive processes to solve a known project scheduling problem. This problem takes into consideration an optimization objective priority for project managers: to maximize the effectiveness of the sets of human resources assigned to the project activities. The hybrid evolutionary algorithm integrates self-adaptive processes with the aim of enhancing the evolutionary search. The behavior of these processes is self-adaptive according to the state of the evolutionary search. The performance of the hybrid evolutionary algorithm is evaluated on six different instance sets and then is compared with that of the best algorithm previously proposed in the literature for the addressed problem. The obtained results show that the hybrid evolutionary algorithm considerably outperforms the previous algorithm.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

An Evolutionary Hyper-Heuristic for the Software Project Scheduling Problem

Software project scheduling plays an important role in reducing the cost and duration of software projects. It is an NP-hard combinatorial optimization problem that has been addressed based on single and multi-objective algorithms. However, such algorithms have always used fixed genetic operators, and it is unclear which operators would be more appropriate across the search process. In this paper, we propose an evolutionary hyper-heuristic to solve the software project scheduling problem. Our novelties include the following: (1) this is the first work to adopt an evolutionary hyper-heuristic for the software project scheduling problem; (2) this is the first work for adaptive selection of both crossover and mutation operators; (3) we design different credit assignment methods for mutation and crossover; and (4) we use a sliding multi-armed bandit strategy to adaptively choose both crossover and mutation operators. The experimental results show that the proposed algorithm can solve the software project scheduling problem effectively

Searching relevant variable subsets in complex systems using K-means PSO

The Relevance Index method has been shown to be effective in identifying Relevant Sets in complex systems, i.e., variable sub-sets that exhibit a coordinated behavior, along with a clear independence from the remaining variables. The need for computing the Relevance Index for each possible variable sub-set makes such a computation unfeasible, as the size of the system increases. Because of this, smart search methods are needed to analyze large-size systems using such an approach. Niching metaheuristics provide an effective solution to this problem, as they join search capabilities to good exploration properties, which allow them to explore different regions of the search space in parallel and converge onto several local/global minima. In this paper, we describe the application of a niching metaheuristic, K-means PSO, to a set of complex systems of different size, comparing, when possible, its results with the ground truth represented by the results of an exhaustive search, while we rely on the analysis of a domain expert to assess the results of larger systems. In all cases, we also compare the results of K-means PSO to another metaheuristic, based on a niching genetic algorithm, that we had previously developed

Automatic detection of learning styles: state of the art

A learning style describes the attitudes and behaviors, which determine an individual´s preferred way of learning. Learning styles are particularly important in educational settings since they may help students and tutors become more self-aware of their strengths and weaknesses as learners. The traditional way to identify learning styles is using a test or questionnaire. Despite being reliable, these instruments present some problems that hinder the learning style identification. Some of these problems include students´ lack of motivation to fill out a questionnaire and lack of self-awareness of their learning preferences. Thus, over the last years, several approaches have been proposed for automatically detecting learning styles, which aim to solve these problems. In this work, we review and analyze current trends in the field of automatic detection of learning styles. We present the results of our analysis and discuss some limitations, implications and research gaps that can be helpful to researchers working in the field of learning styles.Fil: Feldman, Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Monteserin, Ariel José. 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