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15 results on '"Gómez S., Claudia"'

5. Classic Cryptanalysis Applied to Exons and Introns Prediction

Author

Aguilar R., Manuel, Fraire H., Héctor, Cruz R., Laura, González B., Juan J., Castilla V., Guadalupe, Gómez S., Claudia G., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Gervasi, Osvaldo, editor, and Gavrilova, Marina L., editor

Published
2007
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6. A Model for the Distribution Design of Distributed Databases and an Approach to Solve Large Instances

Author

Fraire H., Héctor, Castilla V., Guadalupe, Hernández R., Arturo, Gómez S., Claudia, Mora O., Graciela, Godoy V., Arquimedes, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Pal, Ajit, editor, Kshemkalyani, Ajay D., editor, Kumar, Rajeev, editor, and Gupta, Arobinda, editor

Published
2005
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11. Project portfolio selection with scheduling: an evolutionary approach.

Author

Martínez-Vega, Daniel A., Cruz-Reyes, Laura, Nelson, Rangel V., Gómez S., Claudia G., Sánchez S., Patricia, and Pérez V.^, Mercedes

Subjects
PRODUCTION scheduling, MIXED integer linear programming, METAHEURISTIC algorithms
Abstract

This paper addresses the project portfolio selection problem enriched with scheduling. The factors considered for project scheduling are: the planning time horizon and the negative impact of the project completion time on the total profit. The objective is to select a subset of projects which maximizes the discounted total gain by late completion time, respecting resource constraints and without exceeding the time horizon. A mixed integer linear programming model was formulated and compared to two recent models addressing the same problem. To show the potential of the model on the large scale we used a metaheuristic based on the genetic algorithm Non-dominated Sorting Genetic Algorithm II. We show experimentally the benefits of our proposal and leave open the possibility of its study applied on a larger scale in future works. [ABSTRACT FROM AUTHOR]

Published
2019

14. Reduction of Decision Rules for Project Explanation on Public Project Portfolio.

Author

Cruz-Reyes, Laura, Medina-Trejo, César, Lopez-Irarragorri, Fernando, Rivera, Gilberto, Gómez S., Claudia G., and Pérez-Villafuerte, Mercedes

Subjects
RULES, ALGORITHMS
Abstract

The selection of a public project portfolio among a set of good portfolios directly impacts public organizations in a decisive manner. This task depends on the criteria of the decision maker and is especially hard when the solution is evaluated on many objectives. This combinational optimization problem has been addressed by multicriteria optimization algorithms which do not generate a single solution, but instead they generate a set of good solutions in the Pareto frontier. In this paper we propose to aid the recommendation of public projects with the utilization of simplified decision rules to explain the construction of the recommended project portfolio. Due to most decision problems can be represented in decision tables, we can form a decision table -- in portfolio selection problem -- with rules defined by the features of the projects (condition attributes) and the decision of support (the decision attribute). To facilitate the recommendation process, we simplify the decision rules via a hybrid rough set-based method that combines a genetic algorithm with an exact method. Less decision rules help the decision maker to faster analyze why some projects are accepted or rejected in a particular portfolio and --by this mean-- get more certainty about his/her decision. With the proposed approach, there is a significant reduction of the attributes of the decision rules with a high accuracy of classification, which was proven on a set of the UCI repository for machine learning test. [ABSTRACT FROM AUTHOR]

Published
2015

15. A Model for the Distribution Design of Distributed Databases and an Approach to Solve Large Instances.

Author

Pal, Ajit, Kshemkalyani, Ajay D., Kumar, Rajeev, Gupta, Arobinda, Fraire H., Héctor, Castilla V., Guadalupe, Hernández R., Arturo, Gómez S., Claudia, Mora O., Graciela, and Godoy V., Arquimedes

Abstract

In this paper we approach the solution of large instances of the distribution design problem. Traditional approaches do not consider that the size of the instances can significantly affect the efficiency of the solution process. This paper shows the feasibility to solve large scale instances of the distribution design problem by compressing the instance to be solved. The goal of the compression is to obtain a reduction in the amount of resources needed to solve the original instance, without significantly reducing the quality of its solution. In order to preserve the solution quality, the compression summarizes the access pattern of the original instance using clustering techniques. In order to validate the approach we tested it on a new model of the replicated version of the distribution design problem that incorporates generalized database objects. The experimental results show that our approach permits to reduce the computational resources needed for solving large instances, using an efficient clustering algorithm. We present experimental evidence of the clustering efficiency of the algorithm. [ABSTRACT FROM AUTHOR]

Published
2005
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