Catálogo de publicaciones - libros

Based on the information processing mechanism of immune system in biotic science, the process of the vaccination was analyzed. Then a new approach of immune algorithm problems for job-shop scheduling was proposed. This method can make self-adjustment of the immune responses along with the cultivation period of antibodies, and accelerate or suppress the generation of antibodies. Furthermore, it can gradually enhance recovery ability of the system, and find the optimal solution with more efficiency. Simulation results show that it is an effective approach.

Inspired by the learning mechanism of the biological immune system, the paper presents a method for weather forecast. Expressions of antigen and B-cell are defined. An immune-based supervised learning algorithm is described in detail. A weather forecast system based on immune theory has thus been presented. The experimental results show that the proposed method has higher forecast accuracy rate than neural network based weather forecast technique.

Dynamically evolutive models and recursive equations for self, antigen, dynamic computer forensics, immune tolerance, mature-lymphocyte lifecycle and immune memory are presented. Following that, a new model, referred to as for computer network surveillance and dynamic computer forensics is proposed. Simulation results show that the proposed model has the features of real-time processing, self-learning, self-adaptivity, and diversity, thus providing a good solution for computer network surveillance and dynamic computer forensics.

This paper proposes a novel dynamic clustering algorithm called DCBAIN, which based on the artificial immune network and immune optimization algorithm. The algorithm includes two phases, it begins by running artificial immune network to find a clustering feasible solution (CFS), then it employs antibody clone algorithm (ACA) to get the optimal cluster number and cluster centers on the CFS. Some experimental results show that new algorithm has satisfied convergent probability and convergent speed.

A novel immunity-based genetic algorithm is proposed to resolve Qos multicast routing effectively and efficiently in this paper. The idea of immunity is mainly realized through two steps based on reasonably selecting vaccines, i.e., a vaccination and an immune selection. Experimental results show that this algorithm can find optimal solution quickly and has a good scalability.

A novel artificial immune system algorithm, Immune Forgetting Clonal Programming Algorithm (IFCPA), is put forward. The essential of the clonal selection inspired operations is producing a variation population around the antibodies according to their affinities, and then the searching area is enlarged by uniting the global and local search. With the help of immune forgetting inspired operations, the new algorithm abstract certain antibodies to a forgetting unit, and the antibodies of clonal forgetting unit do not participate in the successive immune operations. Decimal coding with limited digits makes IFCPA more convenient than other binary-coded clonal selection algorithms in large parameter optimization problems. Special mutation and recombination methods are adopted in the antibody population’s evolution process of IFCPA in order to reflect the process of biological antibody gene operations more vividly. Compared with some other Evolutionary Programming algorithms such as Breeder Genetic Algorithm, IFCPA is shown to be an evolutionary strategy which has the ability for solving complex large parameter optimization problems, such as high-dimensional Function Optimizations, and has a higher convergence speed.

In this study, diagnosis of breast cancer, the second type of the most widespread cancer in women, was performed with a new approach, FS-AIRS (Feature Selection Artificial Immune Recognition System) algorithm that has an important place in classification systems and was developed depending on the Artificial Immune Systems. With this purpose, 683 data in the Wisconsin breast cancer dataset (WBCD) was used. In this study, differently from the studies in the literature related to this concept, firstly, the feature number of each data was reduced to 6 from 9 in the feature selection sub-program by means of forming rules related to the breast cancer data with the C4.5 decision tree algorithm. After separating the 683 data set with reduced feature number into training and test sets by 10 fold cross validation method in the second stage, the data set was classified in the third stage with AIRS and a quite satisfying result was obtained with respect to the classification accuracy compared to the other methods used for this classification problem.

A novel artificial immune strategies based data storage model, called AIS-DS, is proposed for dealing with the problem of resources sharing in a storage area network (SAN). Especially for the multi-user’s tasks, this technology has some essential features for ensuring the security and privacy of information and/or data, because a SAN here can be regarded exclusive for each user with its own vaccines (a kind of special codes assigned for this user), and on the other hand, damage or interference to the disk or type to some extent in a local area of SAN, will not destroy the integrity of the saved data. Furthermore, with AIS-DS, the privacy of user’s coded/decoded data is guaranteed even if the disk is physically handed by some other unwanted users.

Artificial Immune Systems (AIS), which are inspired from nature immune system, have recently been investigated for many information processing applications, such as feature extraction, pattern recognition, machine learning and data mining. In this paper, we investigate AIS, and in particular the clonal selection algorithm for Associative Classification (AC). To implement associative classification effectively, we need to tackle the problems on the very large search space of candidate rules during the rule mining process. This paper proposes a new approach known as AIS-AC for mining association rules effectively for classification. In AIS-AC, we treat the rule mining process as an optimization problem of finding an optimal set of association rules according to some predefined constraints. The proposed AIS-AC approach is efficient in dealing with the complexity problem on the large search space of rules. It avoids searching greedily for all possible association rules, and is able to find an effective set of associative rules for classification.

This investigation studies the applicability of using mobile robots with artificial immune algorithm (AIA) based obstacle-avoiding path planning inside a specified environment in real time. Path planning is an important problem in robotics. AIA is applied to determine the position and the angle between a mobile robot, an obstacle and the goal in a limited field. The method seeks to find the optimal path. The objectives are to minimize the length of the path and the number of turns. The results of the real-time experiments present the effectiveness of the proposed method.