Figure 3 - uploaded by Satoshi Endo
Content may be subject to copyright.
Source publication
The objective is to propose an immune distributed competitive
problem solver using MHC and an immune network and to verify its
validity by means of computer simulations. Our algorithm solves the
division-of-labor issues and problems for each agent work domain in a
multi-agent system (MPS) by two immune functions. First, the Major
Histocompatibility...
Context in source publication
Citations
... As the diverse types of self-MHC present different types of peptides, the permutation mask allows multiple representations of detectors. Toma et al. (2000) used the internal state of mobile agents as self-MHC and the interaction with external information as self-MHC/peptide bindings. To the best of our knowledge, CIFD is the first AIS to employ self-MHC in order to provide a selfrestriction feature and increase the diversity of detectors. ...
The retail sector often does not possess sufficient knowledge about potential or actual frauds. This requires the retail sector to employ an anomaly detection approach to fraud detection. To detect anomalies in retail transactions, the fraud detection system introduced in this work implements various salient features of the human immune system. This novel artificial immune system, called CIFD (Computer Immune system for Fraud Detection), adopts both negative selection and positive selection to generate artificial immune cells. CIFD also employs an analogy of the self-Major Histocompatability Complex (MHC) molecules when antigen data is presented to the system. These novel mechanisms are expected to improve the scalability of CIFD, which is designed to process gigabytes or more of transaction data per day. In addition, CIFD incorporates other prominent features of the HIS such as clonal selection and memory cells, which allow CIFD to behave adaptively as transaction patterns change.
抄録
Recently, novel information processing mechanisms inspired by biological immune system are proposed and showed better capability than Neural Network and Genetic Algorithm. Biological immune system consists of high information processing mechanisms such as diverse antibody production mechanisms, self-regulating mechanism and primary and secondary immune responses based on antigen's specificity and immunological memory. Immune Algorithm (IA) based on this biological immune system can obtain multi optimal solutions without the constraint. However, in order to require very much computational time, it is not suitable for the actual problems. Therefore, the dedicated hardware is important in order to apply IA to actual problems. In this paper, we proposed Immune Algorithm processor for nurse scheduling problem. The proposed architecture is realized parallel processing on the calculation for affinity degree. Furthermore, the architecture is proposed not only the pipeline at evaluation phase, but also the pipeline on the whole at reconstruction phase. Experiment result evaluating the proposed architecture was shown to achieve more than 20 times the speed keeping the scheduling quality compared with software processing.
Based on the analysis of the flaw mechanism of tradition genetic algorithm and critically inheriting the immunity theory foundation, the paper proposes a new optimized algorithm - Subregion Artificial immunity Optimized Algorithm Based on Parallel, which can speed up the partially convergence and at the same time maintain the global convergence through the simulation of actual immunity behavior of organism. The basic thought of this algorithm is firstly to divide a complex question into several simple questions, secondly to carry out parallel "immunity" computation according to those simple questions, thirdly to pour the results (the antibodies) into the global immunity algorithm, which not only simplifies the complex question, but also raises the efficiency of the algorithm.
The purpose of this paper is to propose an extended immune optimization algorithm using division as well as integration processing
based on immune cell-cooperation and to investigate its validity by computer simulations. In the biological immune system,
the immune cell-cooperation is a framework including MHC and immune network, the function of which is to eliminate unknown
vast antigens. Our algorithm solves the division-of-labor problems for each agent’s work domain inside the multi-agent system
(MAS) through interactions between two agents, and those of between agents and environment through the work of immune functions.
There are three functions in our algorithm: the division as well as integration processing and the co-evolutionary-like approach.
The division as well as integration processing optimizes the work domain, and the co-evolutionary approach realizes equal
divisions. In order to investigate the validity of the proposed method, this algorithm is applied to the “Nth agent’s Travelling
Salesmen Problem (called the n-TSP)” as a typical problem of multi-agent system. The property that is believed to function
as solution driver for MAS shall be clarified using several simulations.
KeywordsOptimization–MHC and immune network–immune cell-cooperation–multi-agent system–division-of-labor problems
A new descriptor, called vector of topological and structural information for coded and noncoded amino acids (VTSA), was derived by principal component analysis (PCA) from a matrix of 66 topological and structural variables of 134 amino acids. The VTSA vector was then applied into two sets of peptide quantitative structure-activity relationships or quantitative sequence-activity modelings (QSARs/QSAMs). Molded by genetic partial least squares (GPLS), support vector machine (SVM), and immune neural network (INN), good results were obtained. For the datasets of 58 angiotensin converting enzyme inhibitors (ACEI) and 89 elastase substrate catalyzed kinetics (ESCK), the R
2, cross-validation R
2, and root mean square error of estimation (RMSEE) were as follows: ACEI, R
cu2⩾0.82, Q
cu2⩾0.77, E
rmse⩽0.44 (GPLS+SVM); ESCK, R
cu2⩾0.84, Q
cu2⩾0.82, E
rmse⩽0.20 (GPLS+INN), respectively.
The protein major histocompatibility complex (MHC) plays an important role in immune systems, the benefit of the MHC polymorphism in immune response is due to its critical influence on the selection and evolution of the antibody. This paper presents an immune optimization algorithm based on the MHC regulation function (IOAMHC) in immune responses. The work presented here build upon previous evolutionary algorithm and clonal selection principle for optimization. In the IOAMHC, the MHC is used to guide the evolution of the antibody, so as to accelerate optimization. The experiment results on the traveling salesman problem (TSP) show that the IOAMHC has much higher convergence speed and better optimization results than that of classical optimization algorithms. The performance of the IOAMHC parameters has also been discussed in this paper
In this paper, an improved self-organizing map approach to solving the traveling salesman problem is proposed by fixing the number of nodes in the output layer of neural network, modifying the neighborhood function, and modifying the weight update rules. An overview of previous work on solving the traveling salesman problem is given. An extension of the proposed algorithm can also be used to solve multiple traveling salesman problems and robot path planning. The simulation results demonstrate that the proposed algorithm is capable of providing a better solution within a reasonable time and much faster than conventional self-organizing map algorithms.
The purpose of this paper is to evaluate an immune optimization algorithm using a biological immune co-evolutionary phenomenon and cell-cooperation. The co-evolutionary model searches solutions through the interactions between two kinds of agents, on the agent is called immune agent, which optimizes the cost of its own work. The other is call antigen agent, which realized the equal work assignment. This algorithm solves the division-of-labor problems in multi-agent system (MAS) through the three kinds of interactions: division-and-integration processing is used for optimization of the work-cost of immune agents and immune cell-cooperation is used to perform equal work assignment as a result of evolving the antigen agents. To investigate the validity, this algorithm is applied to "n-th agent's traveling salesman problem" as a typical problem of MAS. The good property on solving for MAS will be clarified by some simulations.
The purposes of the paper are to propose and evaluate an immune optimization algorithm inspired by biological immune cell-cooperation, and this algorithm solves the division-of-labor problems in a multi-agent system (MAS). The proposed algorithm solves the problem through interactions between agents, and between agents and the environment. The interactions are performed by division-and-integration processing, inspired by immune cell-cooperation and a similar co-evolutionary approach. The division-and-integration processing optimizes the work domain, and the similar co-evolutionary approach performs equal divisions. To investigate the validity, this algorithm is applied to "N-th agent's Travelling Salesmen Problem" as a typical problem of MAS. The best property for solving via MAS is clarified with some simulations
