Wenxing Bao · 2010
To obtain the best image threshold fast and effectively,based on clonal selection principle of artificial immune system,a novel messy genetic algorithm is presented based on the method of maximum classes square error for image threshold segmentation problem. To prevent running into local classic solutions,emerging premature convergence,the probability selection in standard genetic al-gorithm is replaced by clonal selection,according to antibody-antigen affinity,the excellent individuals in population are cloned and multiplicated selectively,and chroma modulatory mechanism is used to restrain high chroma individuals,promote low chroma individuals and keep diversity of individuals in population. Emulational experimental results show that this method has good segmentation effects and strong practicability.
Cited by 0
Yongchang Ren · 2025
Aiming at the challenges such as the difficulty in identifying new variant viruses in computer virus detection, the slow convergence speed of traditional algorithms, and the limited edge computing resources, a new virus detection paradigm integrating quantum computing and improved CSA is proposed. Firstly, the global search ability of the algorithm is enhanced through dynamic affinity calculation and a two-stage clone amplification strategy. The quantum superposition state mutation operator and the quantum tunneling effect adaptive mutation rate adjustment mechanism are introduced to break through the local optimal bottleneck of the classical algorithm in the high-dimensional solution space. Secondly, the storage structure of the immune memory bank is optimized by utilizing the characteristics of quantum entanglement to achieve parallel retrieval and distributed update of the historical optimal solution, reducing the memory occupation on edge devices to 1/5 of the traditional method. Experiments show that although CAS is slightly inferior to the CNN algorithm in terms of detection accuracy rate, false alarm rate and missed alarm rate, it has certain advantages in detection speed and is significantly better than FCM. The research results have broken through the limitations of traditional detection technologies and achieved effective detection of unknown viruses.
Cited by 0
Yubo Hu, Tiejun Chen · 2008
In this paper, we propose a new multi-objective optimization approach based the clonal selection principle which is from an artificial immune system. Our approach uses the cluster method in the memory cell set of the clonal selection principle to renew and eliminate antibody; the non-uniform mutation operator is employed to the multiplicity of population. This algorithm cannot promote to individual competition and universality of populations by using antibody-antibody affinity but restrain excessive competition by using antigen-antigen affinity. In our study, a test function and some metrics commonly adopted in multi-objective optimization are used. Our results indicate that the proposed approach has its performances better than those produced by NSGA-square, and closer to Pareto-curve which is a set of the uniform and widespread solutions.
Cited by 0
Felipe Campelo, Frederico Gadelha Guimarães, Hajime Igarashi et al. · IEEE Transactions on Magnetics · 2005
This paper proposes the real-coded clonal selection algorithm (RCSA) for use in electromagnetic design optimization. Some features of the algorithm, such as the number of clones, mutation range, and the fraction of the population selected each generation are discussed. The TEAM Workshop problem 22 is investigated, in order to illustrate the performance of the algorithm in a real electromagnetic problem. The results obtained, a set of optimal solutions representing a broader range of options for the designer, are compared with those achieved by a genetic algorithm, showing the efficiency of the RCSA in practical optimization problems.
Cited by 128
Xiangrong Zhang, Erlei Zhang, Runxin Li · 2012
A new method of feature extraction based on immune clonal selection algorithm is proposed, in which the immune clonal selection algorithm is used to optimize the projection vector. Some orthogonal bases are randomly selected as the initial basis vector sets from the original feature space, and the direction of the basis vectors is optimized to generate the optimal projection vector using the immune clonal selection algorithm. This method provides a new scheme of applying the immune clonal algorithm to feature extraction. Experimental results on benchmark datasets and MSTAR dataset for SAR target recognition verify the effectiveness of the proposed method.
Cited by 4
王经卓, 龚成龙, 纪志成 et al. · 2015
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Yılmaz Atay, Halife Kodaz · Lecture Notes on Software Engineering · 2013
The Clonal Selection Algorithm is one of the most important algorithms of Artificial Immune Systems. In addition, it is has taken place in multiple studies in recent years. In this study, most visited web pages have been identified from internet access file by using a Clonal Selection Algorithm. In here, Clonal Selection Algorithm is applied to web mining association analysis. Necessary changes according to the association rules have been made in selection and mutation mechanisms of the Clonal Selection Algorithm. Results showed that the proposed method can be applied successfully for association rules.
Cited by 0Open access
Emad Nabil, Amr Badr, Ibrahim Farag · 2011
Cited by 2
Kim King's College, J. Kim · 2002
The dynamic clonal selection algorithm (DynamiCS) was created to tackle the difficulties of anomaly detection in continuously changing environments (Kim and Bentley, 2002). This paper describes an extension to the original algorithm, involving the deletion of memory detectors that are no longer valid. Experiments are performed on the extended system and results are analysed. The results show a marked decrease in false positive errors produced by the system.
Cited by 0
Hongwei Dai, Yang Yu · Lecture notes in computer science · 2016
Cited by 2
Muhammad Taufik Rizky · 2010
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Yi Wang, Tao Li, Xiaojie Liu et al. · 2022 IEEE Congress on Evolutionary Computation (CEC) · 2022
Feature selection algorithms aim to improve the per-formance of machine learning algorithms by removing irrelevant and redundant features. Various feature selection algorithms have been proposed, but most of them select a global feature subset for characterizing the entire sample space. In contrast, this study proposes an efficient discrete clonal selection algorithm for local feature selection called DCSA-LFS with three features: (1) local sample behaviors are considered, and a local clustering-based evaluation criterion is used to select a distinct optimized feature subset for each different sample region; (2) an improved discrete clonal selection algorithm is proposed, which uses a differential evolution-based mutation operator to enhance the search capability of clonal selection algorithms; and (3) a two-part antibody representation is adopted to automatically adjust the weight-related parameter. Experimental results on twelve UCI datasets show that DCSA-LFS is competitive with traditional filter-based feature selection algorithms and a clonal selection algorithm-based local feature selection algorithm.
Cited by 4
Li Zhang, Xiangru Meng, Weijia Wu et al. · 2009
In order to select the most predictive features from network sample data for fault diagnosis, a novel adaptive immune clonal selection algorithm (AICSA) is proposed. By simulating the mechanisms of biological immune system such as immune memory, clone selection and self-adaptation, AICSA achieves the dynamic control of evolution process, which realizes global optimal computing combined with the local searching. Compared with traditional evolutionary algorithms, the proposed algorithm not only has higher convergence rate and better searching ability, but also can avoid prematurity and degeneration phenomenon. The experimental results show that feature selection for machine learning is necessary, and AICSA can efficiently reduce the number of features while improving the performance of network fault diagnosis based on SVM.
Cited by 7
Heng Jie Li, Hao Xiao, Xianjun Du et al. · Advanced materials research · 2012
In order to realize effective tracking of output of non-linear plants with model uncertainty in specified time domain, a clonal selection algorithm based fuzzy optimal iterative learning control algorithm is proposed. In the algorithm, a clonal selection algorithm is employed to search optimal input for next iteration, and another clonal selection algorithm is used to update the parameters of Takagi-Sugeno-Kang fuzzy system model of the plant. Simulations show that the proposed method converges faster than GA-ILC in iterative domain，and is able to deal with model uncertainty well
Cited by 0
Xingbao Liu, Liangwu Shi, Rongyuan Chen et al. · 2009
In order to increase the diversity of immune algorithm when solving high-dimensional global optimization problems, a novel clonal selection algorithm with randomized clonal expansion strategy(RCSA) is proposed. The main characteristic of RCSA is clonal expansion. In addition, a novel performance evaluation criterion is constructed in this paper, by which the performance of different population-based algorithms can be compared easily. In the experimental study, firstly we obtain an appropriate value of the ratio of clonal expansion through some traditional test functions. Next several conventional clonal selection algorithms are used to validate the performance of proposed RCSA. The experimental results of the RCSA are significantly better than that of the conventional CSAs.
Cited by 6
Man Sun Yu, Dongdong Zhao, Dian Zhou et al. · 2019
With the security breaches in Internet of Things devices, the detection of firmware vulnerability is more crucial than ever. Presently, many methods for firmware vulnerability detection have been proposed, but there are still some room for improvement on the detection precision. In this paper, we propose to use clonal selection algorithm to detect vulnerability functions in firmware. Firstly, we use the Relief algorithm to select the features that are more suitable for clonal selection algorithm. Then, we utilize principal component analysis algorithm to calculate the weights of the features. In the process of detection, we establish a set of specific detectors for each vulnerability function. In the end, we detect the vulnerability functions through these specific detectors. The experimental results show that the precision of our approach on detecting real vulnerabilities is competitive to the typical algorithm VDNS which is based on the neural network.
Cited by 2
Jason Brownlee · Swinburne Research Bank (Swinburne University of Technology) · 2007
The minimal clonal selection provides a reduced realisation of the principles of the clonal selection theory, embodying the information processing properties of a parallel mutation-based hill climber and the winner-take-all competitive learning policy. In the work proposing the approach, a set of performance expectations were proposed regarding convergence and resource utilisation of the approach. This work investigates these expectations of the minimal clonal selection algorithm in a equally minimal random antigenic determinant problem domain. The resource utilisation behaviour is demonstrated to occur as expected, although the number of determinants is show to linearly increase the number of exposures the system requires to acquire the full set of patterns from the domain. This variation on the expected consistent convergence behaviour is speculated to be caused by the interactive competition effects of the winner-take-all principle applied across the population of cells.
Cited by 0Open access
Yingjie Zhang, Mao Ci-ping · Journal of Computer Applications · 2013
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Shangce Gao, Winston Wang, Hong Dai et al. · IEICE Transactions on Information and Systems · 2008
Both the clonal selection algorithm (CSA) and the ant colony optimization (ACO) are inspired by natural phenomena and are effective tools for solving complex problems. CSA can exploit and explore the solution space parallely and effectively. However, it can not use enough environment feedback information and thus has to do a large redundancy repeat during search. On the other hand, ACO is based on the concept of indirect cooperative foraging process via secreting pheromones. Its positive feedback ability is nice but its convergence speed is slow because of the little initial pheromones. In this paper, we propose a pheromone-linker to combine these two algorithms. The proposed hybrid clonal selection and ant colony optimization (CSA-ACO) reasonably utilizes the superiorities of both algorithms and also overcomes their inherent disadvantages. Simulation results based on the traveling salesman problems have demonstrated the merit of the proposed algorithm over some traditional techniques.
Cited by 32Open access
Jason Brownlee · Swinburne Research Bank (Swinburne University of Technology) · 2007
Host organisms with acquired immune systems reproduce, perpetuating their population. The creation of progeny hosts by a parental population introduces a generational relationship, which may be reduced to a lock-step population in which the present population is replaced by a created progeny population. This work extends the previously defined host-based population-based algorithm by introducing the generational principle. Extension of a minimal generational algorithm facilitates inter-generational information sharing inspired by maternal immunity and neo-Darwinian evolution.
Cited by 0Open access