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An improved artificial immune algorithm
Abstract
Analyze the reasons of the traditional artificial immune algorithm easily falling into local extreme point or premature convergence in the optimization process. A novel artificial immune algorithm, Adaptive Clone and Suppression Artificial Immune Algorithm (ACSAIA) is put forward. The proposed algorithm takes into account two factors of antibody affinity and concentration of antibody, and gives an adaptive operator to adjust them. Comparing with the corresponding evolutionary algorithm, ACSAIA can enhance the diversity of the population, avoid prematurity and solve deceptive problems to some extent. Moreover, the proposed algorithm has high convergence speed. The experiments show the proposed algorithm is superior to the traditional artificial immune algorithm and standard genetic algorithm in convergence speed and optimization performance.
... One single classifier has been constructed from each of the replicates. The outputs of classifiers are combined using a majority —Large number of parameters [Espejo et al. 2010] —High training time [Bozorgtabar et al. 2011] AIS —Suitable for pattern recognition and optimization due to its simplicity —Can handle the S3 problem [Luh 2011] —Slow convergence [Fu-gang 2010] —High-dimensional networks cannot be easily visualized [Krzysztof et al. 2009] ACO Can find the optimal facial features without any prior knowledge [Kanan et al. 2007] —Convergence is guaranteed —Complex implementation PSO —Low computational cost and high convergence rate —Simple to implement and uses few parameters —Convergence toward local optima [Wang and Xiao 2005] —The difficulty to control diversity ABC —High flexibility and uses few control parameters —Can handle multidimensional and multimodal FR systems [Yan and Li 2011] —Low accuracy [Yan and Li 2011] —Slow convergence [Karaboga et al. 2012] BFO Can achieve good RR using few features [Jakhar et al. 2011] High computational cost [Jakhar et al. 2011] FFNN High accuracy [Prasad et al. 2011] High computational cost SOMs —Can be used in dimensionality reduction tasks [Patole et al. 2010] —Can deal with scale invariance, rotation, and distortion of images —High computational cost —SOM-based clusters might not be stable [Zhang and Zuo 2007] HNN —Fast and simple —Insensitive to little variations in the image [Chand 2010] Binary data constraints SNNs —SNN overcomes limitations on traditional neural networks pertaining to computational cost ...
An increased number of bio-inspired face recognition systems have emerged in recent decades owing to their intelligent problem-solving ability, flexibility, scalability, and adaptive nature. Hence, this survey aims to present a detailed overview of bio-inspired approaches pertaining to the advancement of face recognition. Based on a well-classified taxonomy, relevant bio-inspired techniques and their merits and demerits in countering potential problems vital to face recognition are analyzed. A synthesis of various approaches in terms of key governing principles and their associated performance analysis are systematically portrayed. Finally, some intuitive future directions are suggested on how bio-inspired approaches can contribute to the advancement of face biometrics in the years to come.. 2015. The impact of bio-inspired approaches towards the advancement of face recognition.
The paper describes research towards the use of an artificial
immune system (AIS) for network intrusion detection. Specifically, we
focus on one significant component of a complete AIS, static clonal
selection with a negative selection operator, describing this system in
detail. Three different data sets from the UCI repository for machine
learning are used in the experiments. Two important factors, the
detector sample size and the antigen sample size, are investigated in
order to generate an appropriate mixture of general and specific
detectors for learning non-self antigen patterns. The results of series
of experiments suggest how to choose appropriate detector and antigen
sample sizes. These ideal sizes allow the AIS to achieve a good non-self
antigen detection rate with a very low rate of self antigen detection.
We conclude that the embedded negative selection operator plays an
important role in the AIS by helping it to maintain a low false positive
detection rate
this report (De Castro & Von Zuben, 1999) is intended to present the basic theory and concepts necessary for the development of immune-based systems. It brings an instructive introduction to the mammal immune system and depicts its most relevant aspects from the viewpoint of engineering. Mechanisms like the clonal selection theory, the immune response along with its affinity maturation process and the immune network hypothesis are emphasized. A few computational algorithms were developed and applied to several different types of problems in order to demonstrate how principles gleaned from the immune system can and must be used in the design of engineering tools for solving complex tasks. In addition, it is introduced an emerging area of research, called immune engineering. The immune engineering is comprised of several strategies, like artificial immune systems, immune-based systems, immunogenetic approaches, etc., and is supposed to include any technique developed using ideas from immunology.
This is a pioneering work on the emerging field of artificial immune systems-highly distributed systems based on the principles of the natural system. Like artificial neural networks, artificial immune systems can learn new information and recall previously learned information. This book provides an overview of artificial immune systems, explaining its applications in areas such as immunological memory, anomaly detection algorithms, and modeling the effects of prior infection on vaccine efficacy.
The clonal selection algorithm is used by the natural immune system to define the basic features of an immune response to an antigenic stimulus. It establishes the idea that only those cells that recognize the antigens are selected to proliferate. The selected cells are subject to an affinity maturation process, which improves their affinity to the selective antigens. In this paper, we propose a powerful computational implementation of the clonal selection principle that explicitly takes into account the affinity maturation of the immune response. The algorithm is shown to be an evolutionary strategy capable of solving complex machine- learning tasks, like pattern recognition and multi- modal optimization.
Based on clonal selection theory, the main mechanisms of a clone in the immune system, which are explored in the field of artificial intelligence, are analyzed. An artificial immune system algorithm, antibody clone algorithm, is put forward. Compared with an improved gene algorithm, the new algorithm is shown to be an evolutionary strategy capable of solving complex machine learning tasks, like multi-modal optimization. Using Markov chain theories, it is proved that the antibody clone algorithm is convergent.