José Antonio Martínez Flores

Cellular Processing Algorithms

In this chapter we propose a new class of cellular algorithms. There exists a variety of cellular algorithm approaches but most of them do not structure the search process. In this work we propose a cellular processing approach to solve optimization problems. The main components of these algorithms are: the processing cells (PCells), the communication between PCells, and the global and local stagnation detection. The great flexibility and simplicity of this approach permits pseudo-parallelization of one or several different metaheuristics. To validate our approach, the linear ordering problem with cumulative costs (LOPCC) was used to describe two cellular processing algorithms, whose performance was tested with standard instances. The experimental results show that the cellular processing algorithms increase solution quality up to 3.6% and reduce time consumption up to 20% versus the monolithic approach. Also the performance of these algorithms is statistically similar to those of the state-of-the-art solutions, and they were able to find 38 new best-known solutions (i.e., not previously found by other algorithms) for the instances used. Finally, it is important to point out that these encouraging results indicate that the field of cellular processing algorithms is a new and rich research area.

On the Exact Solution of VSP for General and Structured Graphs: Models and Algorithms

In this chapter the vertex separation problem (VSP) is approached. VSP is NP-hard with important applications in VLSI, computer language compiler design, and graph drawing, among others. In the literature there are several exact approaches to solve structured graphs and one work that proposes an integer linear programming (ILP) model for general graphs. Nevertheless, the model found in the literature generates a large number of variables and constraints, and the approaches for structured graphs assume that the structure of the graphs is known a priori. In this work we propose a new ILP model based on a precedence representation scheme, an algorithm to identify whether or not a graph has a Grid structure, and a new benchmark of scale-free instances. Experimental results show that our proposed ILP model improves the average computing time of the reference model in 79.38 %, and the algorithm that identifies Grid-structured graphs has an effectiveness of 100 %.

Emotional conversational agents in clinical psychology and psychiatry

This paper is based on a project at the University of Barcelona to develop the skills to diagnose the Generalized Anxiety Disorder (GAD) in students of psychology and psychiatry using a chatbot. The problem we address in this paper is to convert a chatbot in an emotional conversational agent capable of generating a believable and dynamic dialogue in natural language. For it, the dialogues convey traits of personality, emotions and its intensity. We propose to make an AIML language extension for the generation of believable dialogue, this extension will allow to create a more realistic scenario for the student to diagnose the condition simulated by the conversational agent. In order to measure the perception of the emotional state of the ECA expressed by the speech acts a survey was applied.

Integer Linear Programming Formulation and Exact Algorithm for Computing Pathwidth

Computing the Pathwidth of a graph is the problem of finding a linear ordering of the vertices such that the width of its corresponding path decomposition is minimized. This problem has been proven to be NP-hard. Currently, some of the best exact methods for generic graphs can be found in the mathematical software project called SageMath. This project provides an integer linear programming model (IPSAGE) and an enumerative algorithm (EASAGE), which is exponential in time and space. The algorithm EASAGE uses an array whose size grows exponentially with respect to the size of the problem. The purpose of this array is to improve the performance of the algorithm. In this chapter we propose two exact methods for computing pathwidth. More precisely, we propose a new integer linear programming formulation (IPPW) and a new enumerative algorithm (BBPW). The formulation IPPW generates a smaller number of variables and constraints than IPSAGE. The algorithm BBPW overcomes the exponential space requirement by using a last-in-first-out stack. The experimental results showed that, in average, IPPW reduced the number of variables by 33.3 % and the number of constraints by 64.3 % with respect to IPSAGE. This reduction of variables and constraints allowed IPPW to save approximately 14.9 % of the computing time of IPSAGE. The results also revealed that BBPW achieved a remarkable use of memory with respect to EASAGE. In average, BBPW required 2073 times less amount of memory than EASAGE for solving the same set of instances.

Comparative Study on Constructive Heuristics for the Vertex Separation Problem

The vertex separation problem (VSP) consists of finding a linear ordering of the vertices of an input graph that minimizes the maximum number of vertex separators at each cut-point induced by the ordering. VSP is an NP-hard problem whose efficient solution is relevant in fields such as very large scale integration design, computer language compiler design, graph drawing and bioinformatics. In the literature reviewed, we found several exact algorithms and two metaheuristics based on the variable neighborhood search approach. These metaheuristics are currently the best stochastic algorithms for solving VSP. One of the key points of their efficiency is the usage of heuristics to construct a high-quality initial solution that considerably improves the algorithm performance. In this chapter we augment the literature on VSP by proposing a new set of heuristics. The proposed constructive heuristics are compared with the best ones found in the state-of-the-art and with random solution generator (Rnd). Experimental results demonstrate the importance of constructive algorithms. The best constructive improves Rnd by 89.96 % in solution quality.

A heterogeneous cellular processing algorithm for minimizing the power consumption in wireless communications systems

In this paper, the NP-hard problem of minimizing power consumption in wireless communications systems is approached. In the literature, several metaheuristic approaches have been proposed to solve it. Currently a homogeneous cellular processing algorithm and a GRASP algorithm hybridized with path-relinking are considered the state of the art algorithms. The main contribution of this paper is the analysis of five main characteristics for a heterogeneous cellular processing algorithm, based on scatter search and GRASP. A series of computational experiments with standard instances were carried out to assess the impact of each one of these characteristics. Among the main analyses we found particularly interesting a time reduction by 74.24%, produced by the stagnation detection characteristic. Also the communication characteristic improves the quality of the solutions by 24.73%. The B Héctor Joaquín Fraire Huacuja automatas2002@yahoo.com.mx J. David Terán-Villanueva david_teran01@yahoo.com.mx Juan Martín Carpio Valadez jmcarpio61@hotmail.com Rodolfo Pazos Rangel r_pazos_r@yahoo.com.mx Héctor José Puga Soberanes pugahector@yahoo.com José A. Martínez Flores jose.mtz@itcm.edu.mx 1 Instituto Tecnológico de León (ITL), Avenida Tecnológico s/No, C.P. 37290, León, Gto, Mexico 2 Instituto Tecnológico de Ciudad Madero (ITCM), Av. 1o. de Mayo s/No esq. Sor Juana Inés de la Cruz, C.P. 89440, Ciudad Madero, Tam, Mexico

Experimental Study of a New Algorithm-Design-Framework Based on Cellular Computing

In this paper the linear ordering problem with cumulative costs (LOPCC) is approached. Currently, a tabu search and a GRASP with evolutionary path-relinking have been proposed to solve it. We propose a new pseudo-parallel strategy based on cellular computing that can be applied to the design of heuristic algorithms. In this paper the proposed strategy was applied on a scatter search algorithm to show the feasibility of our approach. A series of computational experiments of the designed cellular algorithm were carried out to analyze the performance reached with the proposed approach versus its monolithic counterpart. Additionally, several parameters used were analyzed to determine their effect on the performance of the designed algorithms. We consider that the new pseudo-parallel approach used in this work can be applied to design high performance heuristic algorithms.

Experimental Analysis for the Lennard-Jones Problem Solution

In this paper the problem of determining the atomic cluster configurations that minimize the Lennard-Jones potential energy is approached. Traditional studies are oriented to improve the quality of the solution and practically do not present statistical information to support the efficiency of the reported solution methods. Without this type of evidence the effectiveness of these methods might highly be dependent only on the capacity of the available computing resources. In this work it is proposed to incorporate statistical information on the performance of the solution methods. An advantage of this approach is that when the performance tests are standardized and statistically supported, we can take advantage of efficient solution methods that have been tested only in conditions of modest computing resources. An experimental study of the problem is presented in which the generated statistical information is used to identify two potential areas to improve the performance of the evaluated method.