This paper proposes a multiobjective heuristic search approach to support a project portfolio selection technique on scenarios with a large number of candidate projects. The original formulation for the technique requ...
详细信息
ISBN:
(纸本)9781450311786
This paper proposes a multiobjective heuristic search approach to support a project portfolio selection technique on scenarios with a large number of candidate projects. The original formulation for the technique requires analyzing all combinations of candidate projects, which is unfeasible when more than a few alternatives are available. We have used a multiobjective genetic algorithm to partially explore the search space of project combinations and select the most effective ones. We present an experimental study based on four project selection problems that compares the results found by the genetic algorithm to those yielded by a non-systematic search procedure. Results show evidence that the project selection technique can be used in large-scale scenarios and that GA presents better results than simpler search strategy. Copyright is held by the author/owner(s).
The reproduction of the movements of a ship by automated platforms, without the use of sensors providing exact data related to the numeric variables involved, is a non-trivial matter. The creation of an artificial vis...
详细信息
ISBN:
(纸本)9781424478149
The reproduction of the movements of a ship by automated platforms, without the use of sensors providing exact data related to the numeric variables involved, is a non-trivial matter. The creation of an artificial vision system that can follow the cadence of said ship, in six axes of freedom, is the goal of this research. Considering that a real time response is a requisite in this case, it was decided to adopt a Boolean artificial neural network system that could identify and follow arbitrary interest points that could define, as a group, a model of the movement of an observed vessel. This paper describes the development of a prototype based on the Boolean perceptron model WiSARD (Wilkie, Stonham and Aleksander's Recognition Device), that is being implemented in the C programming language on a desktop computer using a regular webcam as input.
The recent evolution of internet technologies, mainly guided by the Extensible Markup Language (XML) and its related technologies, are extending the role of the World Wide Web from information interaction to service i...
详细信息
In this work, we propose a new algorithm to solve a variant of the Vehicle Routing Problem that is the Single Vehicle Routing Problem with Deliveries and Selective Pickups (SVRPDSP). Our algorithm produces good qualit...
详细信息
In this work, we propose a new algorithm to solve a variant of the Vehicle Routing Problem that is the Single Vehicle Routing Problem with Deliveries and Selective Pickups (SVRPDSP). Our algorithm produces good quality solutions that are better than the best known solutions in the literature. In order to reduce the time spent to solve large-sized instances, we also propose here a parallel implementation of our algorithm that explores a heterogeneous environment composed of a CPU and a GPU. Therefore, our algorithm harnesses the tremendous computing power of the GPU to improve the performance of the local searches computation. We obtained average speedups from 2.73 to 16.23 times with our parallel approach.
Music tracking is a useful technique for many music related tasks. Some applications include evaluating musicians performance, automatic score page turning and syncing music lyrics. In this work, we describe a WiSARD-...
详细信息
Music tracking is a useful technique for many music related tasks. Some applications include evaluating musicians performance, automatic score page turning and syncing music lyrics. In this work, we describe a WiSARD-based system for real-time music tracking and evaluate the performance of the corresponding implementation. Given any audio example, the system is capable of recognizing which point of the original signal is currently playing and of dynamically tracking it. In other words, if the music restarts or jumps to another position, the system is capable of following it. This is accomplished thanks to the low complexity training and classifying of the models used, which continually keep track of all possible points of the music, and not only of the current neighboring region. Experiments are provided in order to analyze the performance of the system in three test scenarios: tracking continuous playing, tracking multiple jumps and self predicting errors. The final results demonstrate that the system is efficient even when applied to musics with some level of repetitions and short periods of silence.
暂无评论