Catálogo de publicaciones - libros

We have developed a calculus, called , for describing the aberrance in biological models. Our approach extends the traditional pi calculus to handle aberrant process in the signal transduction. In this paper we propose a typing system that replaces the tag system of Ipi calculus. It is shown that the typing system is equal to the tag system in terms of the expressive power.

In this paper, we propose a novel feature optimization method to build a cascade Adaboost face detector for real-time applications on cellular phone, such as teleconferencing, user interfaces, and security access control. AdaBoost algorithm selects a set of features and combines them into a final strong classifier. However, conventional AdaBoost is a sequential forward search procedure using the greedy selection strategy, redundancy cannot be avoided. On the other hand, design of embedded systems must find a good trade-off between performances and code size due to the limited amount of resource available in a mobile phone. To address this issue, we proposed a novel Genetic Algorithm post optimization procedure for a given boosted classifier, which leads to shorter final classifiers and a speedup of classification. This GA-optimization algorithm is very suitable for building application of embed and resource-limit device. Experimental results show that our cellular phone embedded face detection system based on this technique can accurately and fast locate face with less computational and memory cost. It runs at 275ms per image of size 384×286 pixels with high detection rates on a SANYO cellular phone with ARM926EJ-S processor that lacks floating-point hardware.

This study describes a generation of globally time optimal trajectories for a mobile robot in predefined environment. The primary task in the study is to apply Differential Evolution (DE) method for definition of globally time-optimal trajectories under environmental and dynamical constraints. The planned trajectories are composed of line segments and curve segments. The structures of the curve segments are determined by using only two parameters such as a turn angle and a translation velocity on the curve v. All possible curve segments in parameters range ∈ (0, ]°, v [0,40] inch and a ∈ [-a, a] inch/sec form a curve segments set. Then DE, is used to find time optimal trajectory from this set. Experimental results are given and the results are shown successfully.

This paper presents the hexagon-based Q-leaning for object search with multiple robots. We set up an experimental environment with five small mobile robots, obstacles, and a target object. The robots were out to search for a target object while navigating in a hallway where some obstacles were placed. In this experiment, we used three control algorithms: a random search, an area-based action making (ABAM) process to determine the next action of the robots, and hexagon-based Q-learning to enhance the area-based action making process.

Numerical optimization problems enjoy a significant popularity in chaos theory fields. All major chaotic techniques use such problems for various tests and experiments. However, many of these techniques encounter difficulties in solving some real-world problems which include non-trivial constrains. This paper discusses a closed loop algorithms (CLA) which based on chaos theory. Thus, for many constrained numerical optimization problems it might be beneficial to add a constraint, and make up of closed loop, using feedback theory. Given an initial best function value (BFV), after the first runs computation we subtract variable increment from obtained BFV, and name it as the new value. That the new value subtracts the new BFV in the next runs computation is defined the accessional constraints. Substituting the new BFV in the next runs for the old BFV and go on, until the global solution is searched. Eventually, some difficult test cases illustrate this approach is very available.

The purpose of this paper is to introduce a recently-developed nature-inspired algorithm, harmony search (HS), and to apply the algorithm to water pump switching problem. The HS algorithm is conceptualized using the musical improvisation process of searching for a better state of harmony. This paper describes a HS algorithm-based approach for the optimal switching problem in serial water pumping system. A standard example from the literature is presented to demonstrate the effectiveness of the proposed method, and the results are compared to genetic algorithm and branch & bound method. Computational results indicate that the HS approach becomes a good optimization model for solving water pump switching problem.

The problem of multicast routing with delay constraints is of great interest in the communication area in the last few years and has been proved a NP-Complete problem. A novel multicast routing algorithm based on clone selection operator is proposed to solve the problem. Simulations illustrate that compared with other algorithms, the proposed algorithm has a fast convergence speed and powerful search ability. The result shows that the proposed algorithm can find a better solution in the same time or even the shorter time.

A genetic algorithm-based routing service for building a multicast distributed tree on Simulation Grid is proposed. Different from existing algorithms, the proposed routing algorithm doesn’t demand that all the routers in the whole network have multicast functionality. It can meet federates’ QoS requirements and minimize the bandwidth consumption. We formalize the routing as a multi-objective optimization problem, which is NP-hard, and apply GA (Genetic Algorithm) to solve it. Also, we focus on the two important aspects of encoding & decoding and fitness function construction in our GA, and present the procedure of seeking the optimal paths. Experiment results have showed that the proposed approach is feasible.

Ant colony optimization (ACO) is relatively new computational intelligence paradigm and provides an effective mechanism for conducting a global search. This work proposes a novel classification rule mining algorithm integrating ACO for search strategy and fuzzy set for representation of the rule terms to give the system flexibility to cope with continuous values and uncertainties typically found in real-world applications and improve the comprehensibility of the rules. The algorithm uses a strategy that is different from ‘divide-and-conquer’ and ‘separate-and-conquer’ approaches used by decision trees and lists respectively; and simulates the ants’ searching different food sources by using attribute-instance weighting and an effective pheromone update strategy for mining accurate and comprehensible rules. Obtained results from several real-world data sets are analyzed with respect to both predictive accuracy and simplicity and compared with C4.5Rules algorithm.

A mean-variance model is proposed for portfolio rebalancing optimization problems with transaction costs and minimum transaction lots. The portfolio optimization problems are modeled as a non-smooth nonlinear integer programming problem. A genetic algorithm based on real value genetic operators is designed to solve the proposed model. It is illustrated via a numerical example that the genetic algorithm can solve the portfolio rebalancing optimization problems efficiently.