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Requirement prioritization is a process that allows selection of the “key” candidate requirements, the ones that are the most important for the construction of quality and cost-controlled software. Requirement prioritization brings certain issues and challenges related with the different stakeholders involved in the project, as well as with(More)
Almost all approaches to multiobjective optimization are based on Genetic Algorithms (GAs), and implementations based on Evolution Strategies (ESs) are very rare. Thus, it is crucial to investigate how ESs can be extended to multiobjective optimization, since they have, in the past, proven to be powerful single objective optimizers. In this paper, we(More)
Hybridization of genetic algorithms with local search approaches can enhance their performance in global optimization. Genetic algorithms, as most population based algorithms, require a considerable number of function evaluations. This may be an important drawback when the functions involved in the problem are computationally expensive as it occurs in most(More)
The head shaking that results from robot locomotion is important because it difficults stable image acquisition and the possibility to rely on that information to act accordingly, for instance, to achieve visually-guided locomotion.
The global optimization of mixed integer non-linear problems (MINLP), constitutes a major area of research in many engineering applications. In this work, a comparison is made between an algorithm based on Simulated Annealing (M-SIMPSA) and two Evolutionary Algorithms: Genetic Algorithms (GAs) and Evolution Strategies (ESs). Results concerning the handling(More)
In this paper, we propose an evolutionary algorithm for handling many-objective optimization problems called MyO-DEMR (many-objective differential evolution with mutation restriction). The algorithm uses the concept of Pareto dominance coupled with the inverted generational distance metric to select the population of the next generation from the combined(More)
This paper presents a gait multi-objective optimization system that combines bio-inspired Central Patterns Generators (CPGs) and a multi-objective evolutionary algorithm. CPGs are modeled as autonomous differential equations, that generate the necessary limb movement to perform the required walking gait. In order to optimize the walking gait, four(More)
The main goal of this work is to investigate the use of genetic algorithms for an optimization model concerned with the stiffness of a linearly laminated elastic plate. In this model each lamina is made of an homogeneous and isotropic material, and the optimization variables are the thickness, Young's modulus and Poisson's ratio. The material is(More)