Catálogo de publicaciones - libros

Ensemble method has been deeply studied and widely used in the machine learning communities. Its basic idea can be represented as: A ‘weak’ learning algorithm that performs just slightly better than random guessing can be ‘boosted’ into an arbitrarily accurate ‘strong’ learning algorithm. Inspired from the fascinating idea, the paper used ensemble method to improve the performance of genetic algorithm and proposed an efficient hybrid optimization algorithm: GA ensemble. In GA ensemble, a collection of genetic algorithms are designed to solve the same problem and population of each algorithm is sampled from a solutions pool using bagging method. Experiments on combinatorial optimization problem and GA-deceptive problems show that ensemble method improves the performance of genetic algorithm greatly.

Traditionally, product returns have been viewed as an unavoidable cost of distribution systems. Up to now there are few studies to address the problem of determining the number and location of centralized product return centers where returned products from retailers or end-customers are collected for manufacturers’ or distributors’ repair facilities while considering the distribution system. To fill the void in such a line of research, this paper proposes a nonlinear mixed-integer programming model and a genetic algorithm that can solve the distribution problem with forward and reverse logistics simultaneously. Compared with a partly enumeration method, the numerical analysis shows the effectiveness of the proposed model and its genetic algorithm approach.

To improve evolutionary design of circuits in efficiency, scalability and optimizing capability, a genetic algorithm based approach was proposed. It employs a gate-level encoding scheme supporting flexible changes of functions and interconnections of comprised logic cells, a multi-objective evaluation mechanism of fitness with weight-vector adaptation and circuit simulation, and an adaptation strategy for crossover probability and mutation probability to vary with individuals’ diversity and genetic-search process. It was validated by experiments on arithmetic circuits, obtaining circuits with expected functions, novel structures, and higher performances in gate usage and operating speed as compared with the results of both conventional and evolutionary approaches. Moreover, by studying the circuits evolved for problems of increasing scales, some novel, efficient and generalized principles have been discerned, which are easy to verify but difficult to dig out by human experts with existing knowledge.

The application of the auxiliary particle filter to the robot localization problem is considered. The auxiliary particle filter (APF) is an enhancement of the generic particle filter. However, APF suffers from the impoverishment problem and needs a large number of particles to represent the system posterior probability density function. An evolutionary computing method, the genetic algorithm is introduced into APF to remove early convergence yet improves the quality of potential solutions. Experiment results show that the evolutionary APF algorithm needs fewer particles and is more precise and robust for mobile robot localization in dynamic environment.

There are many non-probabilistic factors that affect the financial markets such that the returns of risky assets may be regarded as fuzzy numbers. This paper discusses the portfolio selection problem based on the possibilistic mean and variance of fuzzy numbers, which can better described an uncertain environment with vagueness and ambiguity to compare with conventional probabilistic mean-variance methodology. Markowitz’s mean-variance model is simplified a linear programming when returns of assets are symmetric triangular fuzzy numbers, so the possibilistic efficient portfolios can be easily obtained by some related algorithms.

The multi-criteria Minimum Spanning Tree problem is an NP-hard problem, and is difficult for the traditional network optimization techniques to deal with. In this paper, a novel genetic algorithm (NGA) is developed to deal with this problem. First, based on the topology of the problem, the proposed algorithm adopts a heuristic crossover operator and a new mutation operator. Then, in order to enhance the ability of exploration of crossover, a new local search operator is designed to improve the offspring of crossover. Furthermore, the convergence of the proposed algorithm to globally optimal solution with probability one is proved. The simulation results indicate that the proposed algorithm is effective.

As the interest about Ubiquitous Computing has been increasing, it is actively processing research which advanced countries try to realize it such as Smart Space, Cool Town, Easy living, TRON project, and so on. The aim of these projects provides user oriented intelligent service considering relationship among main components (user, object, and environment) of a ubiquitous era. In this paper, we propose User-oriented intelligent Multimedia Service system in the Intelligent Home (IHUMS). The proposed system conducts intelligently the context information (user preference, user location, device status, etc.) using smart sensor agent module. It also provides the interoperability of multimedia among incompatible devices, authentication method which is suitable for the Intelligent Home, and transparent and secure service.

In this paper, we present an original meta-learning framework, namely the (Meta INDuctive neuro-FUzzy Learning) system. is based on a neuro-fuzzy learning strategy providing for the inductive processes applicable both to ordinary base-level tasks and to more general cross-task applications. The results of an ensemble of experimental sessions are detailed, proving the appropriateness of the system in managing meta-level contexts of learning.

Location-based services (LBS) depends on data gathered from mobile and ubiquitous devices and use to provide services like getting the appropriate location of a mobile user presented in physical and logical maps. The main operations of location management in LBS are updating and searching or paging. Some studies to improve these were presented by using optimal sequential paging and location area schemes. Different LBS means variety of methods on accessing data that leads to complexity of providing services. In this paper, we use an approach of hierarchical structured agents for the method of locating a mobile object in a location-based service. This study focuses on location management by using agents. Agents were used for accessing the distributed databases on LBS. It also introduces a hierarchical searching method that uses a nearest neighbor technique for fast searching. The result of using the technique shows an improved searching method in the location management.

Model checking has being used mainly to check if a system satisfies the specifications expressed in temporal logic and people pay little attention to the problem for model checking logics of knowledge. However, in the distributed systems community, the desirable specifications of systems and protocols have been expressed widely in logics of knowledge. In this paper, based on the SMV, by the semantics of knowledge and set theory, approaches for model checking logics of knowledge and common knowledge are presented. These approaches make SMV’s functions extended from temporal logics to temporal logics of knowledge. We will illustrate in an example the applications to security verifications for a cryptographic protocol.