Laura Cruz Reyes
Instituto Tecnológico de Ciudad Madero
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Featured researches published by Laura Cruz Reyes.
hybrid artificial intelligence systems | 2010
Laura Cruz Reyes; Carlos Alberto Ochoa Ortíz Zezzatti; Claudia Gómez Santillán; Paula Hernández Hernández; Mercedes Villa Fuerte
In the last years the population of Leon City, located in the state of Guanajuato in Mexico, has been considerably increasing, causing the inhabitants to waste most of their time with public transportation As a consequence of the demographic growth and traffic bottleneck, users deal with the daily problem of optimizing their travel so that to get to their destination on time To give a solution to this problem of obtaining an optimized route between two points in a public transportation, a method based on the cultural algorithms technique is proposed Cultural algorithms are used in the generated knowledge in a set of time periods for a same population, using a belief space These types of algorithms are a recent creation The proposed method seeks a path that minimizes the time of traveling and the number of transfers The results of the experiment show that the technique of the cultural algorithms is applicable to these kinds of multi-objective problems.
mexican international conference on artificial intelligence | 2007
Laura Cruz Reyes; Diana Maritza Nieto-Yáñez; Nelson Rangel-Valdez; Juan Herrera Ortiz; Guadalupe Castilla Valdez; J. Francisco Delgado-Orta
The present paper approaches the loading distribution of trucks for Product Transportation as a rich problem. This is formulated with the classic Bin Packing Problem and five variants associated with a real case of study. A state of the art review reveals that related work deals with three variants at the most. Besides, they do not consider its relation with the vehicle routing problem. For the solution of this new rich problem a heuristic-deterministic algorithm was developed. It works together with a metaheuristic algorithm to assign routes and loads. The results of solving a set of real world instances show an average saving of three vehicles regarding their manual solution; this last needed 180 minutes in order to solve an instance and the actual methodology takes two minutes. On average, the demand was satisfied in 97.45%. As future work the use of a non deterministic algorithm is intended.
hybrid intelligent systems | 2013
Marco Aurelio Sotelo-Figueroa; Héctor José Puga Soberanes; Juan Martín Carpio; Héctor Joaquín Fraire Huacuja; Laura Cruz Reyes; Jorge Alberto Soria Alcaraz
The development of low-level heuristics for solving instances of a problem is related to the knowledge of an expert. He needs to analyze several components from the problem instance and to think out an specialized heuristic for solving the instance. However if any inherent component to the instance gets changes, then the designed heuristic may not work as it used to do it. In this paper it is presented a novel approach to generated low-level heuristics; the proposed approach implements micro-Differential Evolution for evolving an indirect representation of the Bin Packing Problem. It was used the Hard28 instance, which is a well-known and referenced Bin Packing Problem instance. The heuristics obtained by the proposed approach were compared against the well know First-Fit heuristic, the results of packing that were gotten for each heuristic were analized by the statistic non-parametric test known as Wilcoxon Signed Rank test.
international syposium on methodologies for intelligent systems | 2008
Laura Cruz Reyes; José Francisco Delgado Orta; Juan Javier González Barbosa; José Torres Jimenez; Héctor Joaquín Fraire Huacuja; Bárbara Abigail Arrañaga Cruz
This work presents a methodology of solution for the well-known vehicle routing problem (VRP) based on an ant colony system heuristic algorithm (ACS), which is applied to optimize the delivery process of RoSLoP (Routing-Scheduling-Loading Problem) identified in the company case of study. A first version of this algorithm models six variants of VRP and its solution satisfies the 100% of demands of the customers. The new version of the algorithm can solve 11 variants of VRP as a rich VRP. Experiments were carried out with real instances. The new algorithm shows a saving of two vehicles with regard to the first version, reducing the operation costs of the company. These results prove the viability of using heuristic methods and optimization techniques to develop new software applications.
international symposium on parallel and distributed processing and applications | 2007
Laura Cruz Reyes; Juan Javier González Barbosa; David Romero Vargas; Héctor Joaquín Fraire Huacuja; Nelson Rangel Valdez; Juan Herrera Ortiz; Bárbara Abigail Arrañaga Cruz; José Francisco Delgado Orta
In this paper a real and complex transportation problem including routing, scheduling and loading tasks is presented. Most of the related works only involve the solution of routing and scheduling, as a combination of up to five different types of VRPs (Rich VRP), leaving away the loading task, which are not enough to define more complex real-world cases. We propose a solution methodology for transportation instances that involve six types of VRPs, a new constraint that limits the number of vehicles that can be attended simultaneously and the loading tasks. They are solved using an Ant Colony System algorithm, which is a distributed metaheuristic. Results from a computational test using real-world instances show that the proposed approach outperforms the transportation planning related to manual designs. Besides a well-known VRP benchmark was solved to validate the approach.
ieee international conference on high performance computing data and analytics | 2007
Rogelio Ortega Izaguirre; Eustorgio Meza Conde; Claudia Gómez Santillán; Laura Cruz Reyes; Tania Turrubiates López
A great amount of natural and artificial systems can be represented as a complex network, where the entities of the system are related of non-trivial form. Thus, the network topology is the pattern of the interactions between entities. The characterization of complex networks allows analyzing, classifying and modeling the topology of complex networks. The degree distribution is a characterization function used in the analysis of complex networks. In this work a comparative study of the degree distribution for three different instances of the Internet was carried out, with information about the interconnection of domains. The Internet has a degree distribution power-law, that is, it has a great amount of weakly connected domains while a few domains have a great number of connections. Our results show that Internet has a dynamic growing maintaining the degree distribution power-law through the time, independently of the growth in the number of domains and its connections.
nature and biologically inspired computing | 2013
Marco Aurelio Sotelo-Figueroa; Héctor José Puga Soberanes; Juan Martín Carpio; Héctor Joaquín Fraire Huacuja; Laura Cruz Reyes; Jorge A. Soria-Alcaraz
The Bin Packing Problem is a classic optimization problem, over the years many heuristics have been developed to obtain better results. There are many approaches to generating heuristics automatically, those approaches are based Genetic Programming, but the heuristics generated sometimes can not be applied to the problem. Recently in the Artificial Intelligence field, the Grammar Evolution approach emerged, which generated expressions like the generated by Genetic Programming; these algorithms evolve into a grammar based on the Backus Naur Form. In the present work we show a Grammar Evolution based on Differential Evolution, which automatically generated heuristics for the Bin Packing Problem instances. Those heuristics generated by the Grammar Evolution are like the Best-Fit heuristic which was designed by humans. The works goal is to prove that is feasible to use the Grammar Evolution to automatically generate and reusing heuristics which have at least the same performance than the best generated by humans, we also propose a Grammar to improve the results obtained for a Grammar based on Genetic Programming.
Mathematical Problems in Engineering | 2014
Marco Aurelio Sotelo-Figueroa; Héctor José Puga Soberanes; Juan Martín Carpio; Héctor Joaquín Fraire Huacuja; Laura Cruz Reyes; Jorge A. Soria-Alcaraz
In recent years Grammatical Evolution (GE) has been used as a representation of Genetic Programming (GP) which has been applied to many optimization problems such as symbolic regression, classification, Boolean functions, constructed problems, and algorithmic problems. GE can use a diversity of searching strategies including Swarm Intelligence (SI). Particle Swarm Optimisation (PSO) is an algorithm of SI that has two main problems: premature convergence and poor diversity. Particle Evolutionary Swarm Optimization (PESO) is a recent and novel algorithm which is also part of SI. PESO uses two perturbations to avoid PSO’s problems. In this paper we propose using PESO and PSO in the frame of GE as strategies to generate heuristics that solve the Bin Packing Problem (BPP); it is possible however to apply this methodology to other kinds of problems using another Grammar designed for that problem. A comparison between PESO, PSO, and BPP’s heuristics is performed through the nonparametric Friedman test. The main contribution of this paper is proposing a Grammar to generate online and offline heuristics depending on the test instance trying to improve the heuristics generated by other grammars and humans; it also proposes a way to implement different algorithms as search strategies in GE like PESO to obtain better results than those obtained by PSO.
mexican international conference on artificial intelligence | 2011
Marcela Quiroz Castellanos; Laura Cruz Reyes; Jose Torres-Jimenez; Claudia Gómez Santillán; Mario César López Locés; Jesús Eduardo Carrillo Ibarra; Guadalupe Castilla Valdez
Causal inference can be used to construct models that explain the performance of heuristic algorithms for NP-hard problems. In this paper, we show the application of causal inference to the algorithmic optimization process through an experimental analysis to assess the impact of the parameters that control the behavior of a heuristic algorithm. As a case study we present an analysis of the main parameters of one state of the art procedure for the Bin Packing Problem (BPP). The studies confirm the importance of the application of causal reasoning as a guide for improving the performance of the algorithms.
electronics robotics and automotive mechanics conference | 2007
Claudia Gómez Santillán; Tania Turrubiates López; Laura Cruz Reyes; Eustorgio Meza Conde; Rogelio Ortega Izaguirre
In this paper a statistical selection of relevant features is presented. An experiment was designed to select relevant and not redundant features or characterization functions, which allow quantitatively discriminating among different types of complex networks. As well there exist researchers given to the task of classifying some networks of the real world through characterization functions inside a type of complex network, they do not give enough evidences of detailed analysis of the functions that allow to determine if all are necessary to carry out an efficient discrimination or which are better functions for discriminating. Our results show that with a reduced number of characterization functions such as the shortest path length, standard deviation of the degree, and local efficiency of the network can discriminate efficiently among the types of complex networks treated here.