Catálogo de publicaciones - libros

This paper presents a neuro-genetic approach for solving constrained nonlinear optimization problems. Genetic algorithm must its popularity to make possible cover nonlinear and extensive search spaces. On the other hand, artificial neural networks have high computational rates due to the use of a massive number of simple processing elements and the high degree of connectivity between these elements. Neural networks with feedback connections provide a computing model capable of solving a large class of optimization problems. The association of a modified Hopfield network with genetic algorithm guarantees the convergence of the system to the equilibrium points, which represent feasible solutions for constrained nonlinear optimization problems. Simulated examples are presented to demonstrate that proposed method provides a significant improvement.

Inspired by the complementary and dominance mechanism in nature, the Primal-Dual Genetic Algorithm (PDGA) has been proved successful in dynamic environments. In this paper, an important operator in PDGA, primal-dual mapping, is discussed and a new statistics-based primal-dual mapping scheme is proposed. The experimental results on the dynamic optimization problems generated from a set of stationary benchmark problems show that the improved PDGA has stronger adaptability and robustness than the original for dynamic optimization problems.

A hybrid mechanism is a configuration that combines the motions of two characteristically different electric motors by means of a mechanism to produce programmable output. In order to obtain better integrative performances of hybrid mechanism, based on the dynamics and kinematic analysis for a hybrid five-bar mechanism, a multi-objective optimization of hybrid five bar mechanism is performed with respect to four design criteria in this paper. Optimum dimensions are obtained assuming there are no dimensional tolerances and clearances. By the use of the properties of global search of genetic algorithm (GA), an improved GA algorithm is proposed based on real-code. Finally, a numerical example is carried out, and the simulation result shows that the optimization method is feasible and satisfactory in the design of hybrid actuator.

A computer virus is a program that can generate possibly evolved copies of itself when it runs on a computer utilizing the machine’s resources, and by some means each copy may be propagated to another computer in which the copy will have a chance to get executed. And we call a virus instance as a viral agent since it is autonomous during its execution by choosing what action to perform in the computer without a user’s intervention. In the paper we develop a computational model of viral agents based on the persistent Turing machine (PTM) model which is a canonical model for sequential interaction. The model reveals the most essential infection property of computer viruses well and overcomes the inherent deficiency of Turing machine (TM) virus models in expressing interaction. It is conceivable that viral agents have much potential to evolve in various environments according to the model. Therefore we also discuss the evolution of viral agents with two existing relevant works.

This paper presents the experiments which where made with the Clustering and Coevolution to Construct Neural Network Ensemble (CONE) approach on two classification problems and two time series prediction problems. This approach was used to create a particular type of Evolving Fuzzy Neural Network (EFuNN) ensemble and optimize its parameters using a Coevolutionary Multi-objective Genetic Algorithm. The results of the experiments reinforce some previous results which have shown that the approach is able to generate EFuNN ensembles with accuracy either better or equal to the accuracy of single EFuNNs generated without using coevolution. Besides, the execution time of CONE to generate EFuNN ensembles is lower than the execution time to produce single EFuNNs without coevolution.

Since the manual construction of our knowledge-base has several crucial limitations when applied to intelligent systems, mental development has been investigated in recent years. Autonomous mental development is a new paradigm for developing autonomous machines, which are adaptive and flexible to the environment. Language development, a kind of mental development, is an important aspect of intelligent conversational agents. In this paper, we propose an intelligent conversational agent and its language development mechanism by putting together five promising techniques; Bayesian networks, pattern matching, finite state machines, templates, and genetic programming. Knowledge acquisition implemented by finite state machines and templates, and language learning by genetic programming are developed for language development. Several illustrations and usability tests show the usefulness of the proposed developmental conversational agent.

The object of multiple Unmanned Aerial Vehicles(UAVs) cooperative reconnaissance is to employ a limit number of UAVs with different capabilities conducting reconnaissance on a set of targets at minimum cost, without violating real world constraints. This problem is a multi-objective optimization problem. We present a Pareto optimality based multi-objective evolutionary algorithm MUCREA to solve the problem. Integer string chromosome representation is designed which ensures that the solution can satisfy the reconnaissance resolution constraints. A construction algorithm is put forward to generate initial feasible solutions for MUCREA, and Pareto optimality based selection with elitism is introduced to generation parent population. Problem specific evolutionary operators are designed to ensure the feasibilities of the children. Simulation results show the efficiency of MUCREA.

A new contrast enhancement algorithm for image is proposed combing genetic algorithm (GA) with wavelet neural network (WNN). In-complete Beta transform (IBT) is used to obtain non-linear gray transform curve so as to enhance global contrast for an image. GA determines optimal gray transform parameters. In order to avoid the expensive time for traditional contrast enhancement algorithms, which search optimal gray transform parameters in the whole parameters space, based on gray distribution of the image, a classification criterion is proposed. Contrast type for original image is determined by the new criterion. Parameters space is given respectively according to different contrast types, which greatly shrinks parameters space. Thus searching direction of GA is guided by the new parameter space. In order to calculate IBT in the whole image, WNN is used to approximate the IBT. In order to enhance the local contrast for image, discrete stationary wavelet transform (DSWT) is used to enhance detail in an image. Having implemented DSWT to an image, detail is enhanced by a non-linear operator in three high frequency sub-bands. The coefficients in the low frequency sub-bands are set as zero. Final enhanced image is obtained by adding the global enhanced image with the local enhanced image. Experimental results show that the new algorithm is able to well enhance the global and local contrast for image.

In this paper, a novel constrained genetic algorithm is proposed and also successfully applied to the optimization of the active bar placement and feedback gains in intelligent truss structures. Based on the maximization of energy dissipation due to active control action, a mathematical model with constrains is initially developed. Then, according to the characteristics of the optimal problem, a new problem-specific encoding scheme, some special “genetic” operators and a problem-dependent repair algorithm are proposed and discussed. Numerical example of a 72-bar space intelligent truss structure is presented to demonstrate the rationality and validity of this methodology, and some useful conclusions are obtained.

In this study, a double-stage genetic optimization algorithm is proposed for portfolio selection. In the first stage, a genetic algorithm is used to identify good quality assets in terms of asset ranking. In the second stage, investment allocation in the selected good quality assets is optimized using a genetic algorithm based on Markowitz’s theory. Through the two-stage genetic optimization process, an optimal portfolio can be determined. Experimental results reveal that the proposed double-stage genetic optimization algorithm for portfolio selection provides a very feasible and useful tool to assist the investors in planning their investment strategy and constructing their portfolio.