22 resultados para Heurísticas.


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The activity of requirements engineering is seen in agile methods as bureaucratic activity making the process less agile. However, the lack of documentation in agile development environment is identified as one of the main challenges of the methodology. Thus, it is observed that there is a contradiction between what agile methodology claims and the result, which occurs in the real environment. For example, in agile methods the user stories are widely used to describe requirements. However, this way of describing requirements is still not enough, because the user stories is an artifact too narrow to represent and detail the requirements. The activities of verifying issues like software context and dependencies between stories are also limited with the use of only this artifact. In the context of requirements engineering there are goal oriented approaches that bring benefits to the requirements documentation, including, completeness of requirements, analysis of alternatives and support to the rationalization of requirements. Among these approaches, it excels the i * modeling technique that provides a graphical view of the actors involved in the system and their dependencies. This work is in the context of proposing an additional resource that aims to reduce this lack of existing documentation in agile methods. Therefore, the objective of this work is to provide a graphical view of the software requirements and their relationships through i * models, thus enriching the requirements in agile methods. In order to do so, we propose a set of heuristics to perform the mapping of the requirements presented as user stories in i * models. These models can be used as a form of documentation in agile environment, because by mapping to i * models, the requirements will be viewed more broadly and with their proper relationships according to the business environment that they will meet

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Nonogram is a logical puzzle whose associated decision problem is NP-complete. It has applications in pattern recognition problems and data compression, among others. The puzzle consists in determining an assignment of colors to pixels distributed in a N  M matrix that satisfies line and column constraints. A Nonogram is encoded by a vector whose elements specify the number of pixels in each row and column of a figure without specifying their coordinates. This work presents exact and heuristic approaches to solve Nonograms. The depth first search was one of the chosen exact approaches because it is a typical example of brute search algorithm that is easy to implement. Another implemented exact approach was based on the Las Vegas algorithm, so that we intend to investigate whether the randomness introduce by the Las Vegas-based algorithm would be an advantage over the depth first search. The Nonogram is also transformed into a Constraint Satisfaction Problem. Three heuristics approaches are proposed: a Tabu Search and two memetic algorithms. A new function to calculate the objective function is proposed. The approaches are applied on 234 instances, the size of the instances ranging from 5 x 5 to 100 x 100 size, and including logical and random Nonograms

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Logic courses represent a pedagogical challenge and the recorded number of cases of failures and of discontinuity in them is often high. Amont other difficulties, students face a cognitive overload to understand logical concepts in a relevant way. On that track, computational tools for learning are resources that help both in alleviating the cognitive overload scenarios and in allowing for the practical experimenting with theoretical concepts. The present study proposes an interactive tutorial, namely the TryLogic, aimed at teaching to solve logical conjectures either by proofs or refutations. The tool was developed from the architecture of the tool TryOcaml, through support of the communication of the web interface ProofWeb in accessing the proof assistant Coq. The goals of TryLogic are: (1) presenting a set of lessons for applying heuristic strategies in solving problems set in Propositional Logic; (2) stepwise organizing the exposition of concepts related to Natural Deduction and to Propositional Semantics in sequential steps; (3) providing interactive tasks to the students. The present study also aims at: presenting our implementation of a formal system for refutation; describing the integration of our infrastructure with the Virtual Learning Environment Moodle through the IMS Learning Tools Interoperability specification; presenting the Conjecture Generator that works for the tasks involving proving and refuting; and, finally to evaluate the learning experience of Logic students through the application of the conjecture solving task associated to the use of the TryLogic

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An important problem faced by the oil industry is to distribute multiple oil products through pipelines. Distribution is done in a network composed of refineries (source nodes), storage parks (intermediate nodes), and terminals (demand nodes) interconnected by a set of pipelines transporting oil and derivatives between adjacent areas. Constraints related to storage limits, delivery time, sources availability, sending and receiving limits, among others, must be satisfied. Some researchers deal with this problem under a discrete viewpoint in which the flow in the network is seen as batches sending. Usually, there is no separation device between batches of different products and the losses due to interfaces may be significant. Minimizing delivery time is a typical objective adopted by engineers when scheduling products sending in pipeline networks. However, costs incurred due to losses in interfaces cannot be disregarded. The cost also depends on pumping expenses, which are mostly due to the electricity cost. Since industrial electricity tariff varies over the day, pumping at different time periods have different cost. This work presents an experimental investigation of computational methods designed to deal with the problem of distributing oil derivatives in networks considering three minimization objectives simultaneously: delivery time, losses due to interfaces and electricity cost. The problem is NP-hard and is addressed with hybrid evolutionary algorithms. Hybridizations are mainly focused on Transgenetic Algorithms and classical multi-objective evolutionary algorithm architectures such as MOEA/D, NSGA2 and SPEA2. Three architectures named MOTA/D, NSTA and SPETA are applied to the problem. An experimental study compares the algorithms on thirty test cases. To analyse the results obtained with the algorithms Pareto-compliant quality indicators are used and the significance of the results evaluated with non-parametric statistical tests.

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The Traveling Salesman with Multiple Ridesharing (TSP-MR) is a type of the Capacitated Traveling Salesman, which presents the possibility of sharing seats with passengers taking advantage of the paths the salesman travels through his cycle. The salesman shares the cost of a path with the boarded passengers. This model can portray a real situation in which, for example, drivers are willing to share parts of a trip with tourists that wish to move between two locations visited by the driver’s route, accepting to share the vehicle with other individuals visiting other locations within the cycle. This work proposes a mathematical formulation for the problem, and an exact and metaheuristics algorithms for its solution, comparing them.

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The Traveling Salesman with Multiple Ridesharing (TSP-MR) is a type of the Capacitated Traveling Salesman, which presents the possibility of sharing seats with passengers taking advantage of the paths the salesman travels through his cycle. The salesman shares the cost of a path with the boarded passengers. This model can portray a real situation in which, for example, drivers are willing to share parts of a trip with tourists that wish to move between two locations visited by the driver’s route, accepting to share the vehicle with other individuals visiting other locations within the cycle. This work proposes a mathematical formulation for the problem, and an exact and metaheuristics algorithms for its solution, comparing them.

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The Behavioral Finance develop as it is perceived anomalies in these markets efficient. This fields of study can be grouped into three major groups: heuristic bias, tying the shape and inefficient markets. The present study focuses on issues concerning the heuristics of representativeness and anchoring. This study aimed to identify the then under-reaction and over-reaction, as well as the existence of symmetry in the active first and second line of the Brazilian stock market. For this, it will be use the Fuzzy Logic and the indicators that classify groups studied from the Discriminant Analysis. The highest present, indicator in the period studied, was the Liabilities / Equity, demonstrating the importance of the moment to discriminate the assets to be considered "winners" and "losers." Note that in the MLCX biases over-reaction is concentrated in the period of financial crisis, and in the remaining periods of statistically significant biases, are obtained by sub-reactions. The latter would be in times of moderate levels of uncertainty. In the Small Caps the behavioral responses in 2005 and 2007 occur in reverse to those observed in the Mid-Large Cap. Now in times of crisis would have a marked conservatism while near the end of trading on the Bovespa speaker, accompanied by an increase of negotiations, there is an overreaction by investors. The other heuristics in SMLL occurred at the end of the period studied, this being a under-reaction and the other a over-reaction and the second occurring in a period of financial-economic more positive than the first. As regards the under / over-reactivity in both types, there is detected a predominance of either, which probably be different in the context in MLCX without crisis. For the period in which such phenomena occur in a statistically significant to note that, in most cases, such phenomena occur during the periods for MLCX while in SMLL not only biases are less present as there is no concentration of these at any time . Given the above, it is believed that while detecting the presence of bias behavior at certain times, these do not tend to appear to a specific type or heuristics and while there were some indications of a seasonal pattern in Mid- Large Caps, the same behavior does not seem to be repeated in Small Caps. The tests would then suggest that momentary failures in the Efficient Market Hypothesis when tested in semistrong form as stated by Behavioral Finance. This result confirms the theory by stating that not only rationality, but also human irrationality, is limited because it would act rationally in many circumstances