Farhad Samadzadegan, Shahin Rahmatollahi Namin, Mohammad Rajabi · Key engineering materials · 2012
The great number of captured near spectral bands in hyperspectral images causes the curse of dimensionality problem and results in low classification accuracy. The feature selection algorithms try to overcome this problem by limiting the input space dimensions of classification for hyperspectral images. In this paper, immune clonal selection optimization algorithm is used for feature selection. Also one of the fastest Artificial Immune classification algorithms is used to compute fitness function of the feature selection. The comparison of the feature selection results with genetic algorithm shows the clonal selection’s higher performance to solve selection of features.
Cited by 0
Wahidur Rahman, Riasaad Haque Aneek, Md Moinuddin et al. · 2023
A substantial and rising number of patients suffer from cardiovascular diseases, including heart attacks, heart failure, and other related illnesses. This case surge places increasing pressure on healthcare professionals and administrators while patients grapple with growing medical costs. To address these challenges, an automated system is necessary within the healthcare sector. In this paper, we present a cardiovascular health monitoring system that incorporates Machine Learning techniques. The study employed feature selection techniques on a secondary dataset. The study has used a nature-inspired technique, namely the Clonal Selection Algorithm (CSA) and Maximum Relevance Minimum Redundancy (mRMR) technique, to identify the most prominent feature in the detection of cardio-vascular illness. This approach was combined with a collection of Machine Learning classifiers. A group of experimental data has been listed to assess the suggested model's effectiveness. The research findings indicated a maximum accuracy rate of 100% when employing the proposed algorithms and orientations. The study also discusses the performance analysis for CSA and mRMR using a set of performance evaluation matrices. Based on the obtained results, it can be inferred that the suggested model will likely exhibit a high level of effectiveness in identifying cardiovascular diseases.
Cited by 2
Yi Wang, Luo Dan, Jian Yao · Electronics · 2024
Effective gene feature selection is critical for enhancing the interpretability and accuracy of genetic data analysis, particularly in the realm of disease prediction and precision medicine. Most evolutionary feature selection algorithms tend to become stuck in local optima and incur high computational costs, particularly when dealing with the complex and high-dimensional nature of genetic data. To address these issues, this study proposes a multitasking feature selection method based on clone selection for high-dimensional microarray data, which identifies optimal features by transferring useful knowledge across two related tasks derived from the same microarray dataset. First, a dual-task generation strategy is designed, where one task selects features based on the Relief-F method, and the other task is generated from the original features. Second, a new mutation operator is introduced to share useful information between the multiple tasks. Finally, an improved clonal selection algorithm is proposed to strengthen the global and local search abilities. The experimental results on six high-dimensional microarray datasets demonstrate that our method significantly outperforms four state-of-the-art feature selection methods, highlighting its effectiveness and efficiency in tackling complex feature selection problems.
Cited by 1Open access
Yi Li, Zhengxing Sun · 2012
In this paper, we present a novel generative method for human motion tracking. The principle contribution is the development of clonal selection algorithm for pose analysis in latent space of human motion. Firstly, we use ISOMAP to learn the low-dimensional latent space of pose state and a manifold reconstruction method is proposed to establish the smooth mappings between the latent and original space. Pose analysis is performed in this latent space, which results to be more efficient and accurate. Secondly, we apply a new evolutionary approach, clonal selection algorithm (CSA) for pose optimization. Then, we design a CSA based method for pose estimation, which can achieve viewpoint invariant 3D pose reconstruction from static images. Thirdly, in order to make CSA suitable for motion tracking, we propose a sequential CSA (S-CSA) framework by incorporating the temporal continuity information into the traditional CSA. Our methods are demonstrated in different motion types and different image sequences. Experimental results show that our method achieves better results than state-of-art methods.
Cited by 0
Mir Wajahat Hussain, Buddhadeb Pradhan, Xiao‐Zhi Gao et al. · Applied Soft Computing · 2020
Cited by 21
Regan Murugesan, K. Sivasakthi Balan · 2012
This paper proposes improved initialization and modified Clonal selection algorithm, called Positive Selection based Modified Clonal Selection algorithm for solving the Job Shop Scheduling Problem. The new algorithm has the advantage of fast convergence speed and preventing from prematurity. Ample well-studied benchmark instances in job-shop scheduling problems were utilized to evaluate the proposed approach. The computational results show that the proposed algorithm could obtain the superior solutions within reasonable computing times, and the results signified the effectiveness and flexibility of the immune memory in Clonal selection algorithm. Keyword: Job shop scheduling, clonal selection, positive selection, Final Solution library
Cited by 9
Farhad Samadzadegan, Shahin Rahmatollahi Namin, Mohammad Rajabi · Key engineering materials · 2012
The great number of captured near spectral bands in hyperspectral images causes the curse of dimensionality problem and results in low classification accuracy. The feature selection algorithms try to overcome this problem by limiting the input space dimensions of classification for hyperspectral images. In this paper, immune clonal selection optimization algorithm is used for feature selection. Also one of the fastest Artificial Immune classification algorithms is used to compute fitness function of the feature selection. The comparison of the feature selection results with genetic algorithm shows the clonal selection’s higher performance to solve selection of features.
Cited by 1
Yanli Ke, Licheng Jiao, Weida Zhou · 2006
A novel channel estimation algorithm for MIMO system is presented in this paper, which is based on the clonal selection theory and the idea of immune evolution. The effectiveness of our method is demonstrated through the simulation in a MIMO system what is known as Vertical Bell-Labs Layered Space Time system (V-BLAST). Simulation results show that the BER performance of channel estimation based on clonal selection algorithm (CA) with proper algorithm parameters is better than the maximum likelihood (ML) and minimum mean-square error (MMSE) channel estimation algorithms
Cited by 1
Ali Al Bataineh, Devinder Kaur · International Journal of Machine Learning and Computing · 2019
In this paper, the Clonal Selection Algorithm (CSA) is implemented to determine the optimal architecture of a convolutional neural network (CNN) for the classification of handwritten character digits. The efficacy of CNN in image recognition is one of the central motives why the world has woken up to the effectiveness of deep learning. During training, an optimal CNN architecture can extract complex features from the data that is being trained; however, the ideal architecture of a CNN for a specific problem cannot be determined by some standard procedure. In practice, CNN architectures are generally designed using human expertise and domain knowledge. By using CSA, optimal architecture of CNN can be determined autonomously through evolution of hyperparameters of the architecture for a given dataset. In this work, proposed methodology is tested on EMNIST dataset which is an enhanced version of MNIST dataset. The results have proven that the CSA based tuning is capable of generating optimal CNN architectures. Through this proposed technique, the best architecture of CNN for a given problem can be selfdetermined without any human intervention.
Cited by 12Open access
Chao Yang, Zijing Huang, Bibo Jiang et al. · The Journal of Supercomputing · 2023
Abstract At present, many cloning selection algorithms have been studied, and improvements have been made to the cloning, mutation and selection steps. However, there is a lack of research on the optimization of the updating operation steps. The clonal selection algorithm is traditionally updated through a random complement of antibodies, which is a blind and uncertain process. The added antibodies may gather near a local optimal solution, resulting in the need for more iterations to obtain the global optimal solution. To solve this problem, our improved algorithm introduces a crowding degree factor in the antibody updating stage to determine whether there is crowding between antibodies. By eliminating antibodies with high crowding potential and poor affinity, the improved algorithm guides the antibodies to update in the direction of the global optimal solution and ensures stable convergence with fewer iterations. Experimental results show that the overall performance of the improved algorithm is 1% higher than that of the clonal selection algorithm and 2.2% higher than that of the genetic algorithm, indicating that the improved algorithm is effective. The improved algorithm is also transplanted to other improved clonal selection algorithms, and the overall performance is improved by 0.97%, indicating that the improved algorithm can be a beneficial supplement to other improved clonal selection algorithms.
Cited by 5Open access
Kashav Ajmera, Tribhuwan Kumar Tewari · Cluster Computing · 2021
Cited by 29
Xiaojin Hou, Shuang Wang, Licheng Jiao et al. · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
Synthetic aperture radar (SAR) image segmentation is a fundamental problem in SAR image interpretation. SAR images often contain non-texture object and texture object. Level set method, known as deformable model, is a powerful image segmentation technique. It can get accurate contours of non-texture object, but has poor performance in getting contours of texture object. In this paper, a new modified model of level set based on clonal selection algorithm is proposed. We use clonal selection algorithm to choose some pixels near the contour, and then perform a neighborhood modification on the level set function during its evolution. The region texture information, supervising the modification process, is incorporated into the level set framework. This new method is particularly well adapted to detection of texture object of interesting. We illustrated the performance of the new method on SAR images. Furthermore, we compared our method with level set method and the modified model of level set based on standard genetic algorithm (SGA) in texture object detection results and image segmentation results. The experimental results show that incorporating region texture information into the level set framework, consistent texture objects are obtained, and accurate and robust segmentations can be achieved.
Cited by 0
Qun Gu, Hao Xiao, Xianjun Du et al. · TELKOMNIKA Indonesian Journal of Electrical Engineering · 2013
Clonal selection algorithm is improved and proposed as a method to solve optimization problems in iterative learning control. And a clonal selection algorithm based optimal iterative learning control algorithm with random disturbance is proposed. In the algorithm, at the same time, the size of the search space is decreased and the convergence speed of the algorithm is increased. In addition a model modifying device is used in the algorithm to cope with the uncertainty in the plant model. In addition a model is used in the algorithm cope with the uncertainty in the plant model. Simulations show that the convergence speed is satisfactory regardless of whether or not the plant model is precise nonlinear plants. The simulation test verify the controlled system with random disturbance can reached to stability by using improved iterative learning control law but not the traditional control law. DOI: http://dx.doi.org/10.11591/telkomnika.v11i1.1921
Cited by 6
T. Devi Prasad · 2011
This paper presents the calibration results of the C-Town network obtained using an artificial immune algorithm called Clonal Selection Algorithm (CLONALG).The calibration problem was formulated as determining the network model parameters such that the best match between measured and predicted data is obtained. The model parameters to be determined include pipe roughness values, valve closures, nodal demands, pump controls and valve settings. The artificial immune algorithm used in this work evolves a population of candidate solutions using the clonal selection principle. The main components of CLONALG include fitness based cloning and maturation of cloned population. The proposed model was applied to calibrate the C-Town network. The network calibration parameters obtained, when input into the C-Town hydraulic model, produced reasonably good match between the predicted and the observed tank water levels and pump station flows. Various performance measures were calculated and presented in the paper.
Cited by 5
Hongbing Zhu, Jun Wu, Jinguang Gu · International journal of intelligent engineering and systems · 2015
Immune algorithms (IAs) are microscopic view of evolutionary algorithms (EAs) and applied in combinatorial optimization problems. This paper addresses to a clonal selection algorithm (CSA) that is one of the most representative IA and was applied into the protein structure prediction (PSP) on AB off-lattice model, in which the memory B cells of the CSA was innovated by employing different strategies: local search and global search in the phase of the mutation. And the CSA was further improved by adding aging operator to combat the premature convergence. However the pure aging operator didn't achieve effective results and sometimes the optimum solution was eliminated. To resolve this problem, the current best solution was reserved by an antibody and it was not eliminated when its age reached its life span. In our experiments the improved algorithm was compared with the standard CSA and the pure aging CSA, which of the results demonstrated that the improved strategy with the memory B cells and long life aging was very effective to overcome premature convergence and to avoid trapped in the local-best solution, and it was also effective in keeping the diversity of the small size population. On the other hand, one novel hybrid algorithm Quantum Immune(QI), which combines Quantum Algorithm (QA) and Immune Clonal Selection(ICS) Algorithm, has been presented for dealing with multi-extremum and multi-parameter problem based on AB off-lattice model in the predicting 2D protein folding structure. Clonal Selection Algorithm was introduced into the hypermutation operators in the Quantum Algorithm to improve the local search ability, and double chains quantum coded was designed to enlarge the probability of the global optimization solution. It showed that the solution mostly trap into the local optimum, to escape the local best solution the aging operator is introduced to improve the performance of the algorithm. Experimental results showed that the lowest energies and computing-time of the improved Quantum Clonal Selection(QCS) algorithm were better than that of the previous methods, and the QCS was further improved by adding aging operator to combat the premature convergence. Compared with previous approaches, the improved QCS algorithm remarkably enhanced the convergence performance and the search efficiency of the immune optimization algorithm.
Cited by 3Open access
Feng Liu, Xiangping Zhang, Fengxing Zou et al. · 2009
In this paper, the mixed-integer nonlinear programming model is established for hybrid flow-shop scheduling problem (HFSP) with the minimum of makespan as the objective function. In order to reduce the computational complexity, immune clonal selection algorithm (ICSA) is applied to HFSP. The definitions of antibody affinity, comparability and density are given in detail. To improve the ability of global optimization for ICSA, mutliclone operator (mutation, crossover and selection) and grouping strategy are employed. The simulation results indicate that ICSA can obtain preferable effect for the solution to HFSP.
Cited by 9
Wan Tang, Ximin Yang, Xia Xie et al. · 2010
To make an immune-inspired network intrusion detection system (IDS) effective, this paper proposes a new framework, which includes our avidity-model based clonal selection (AMCS) algorithm as core element. The AMCS algorithm uses an improved representation for antigens (corresponding to network access patterns) and detectors (corresponding to detection rules). In particular, a bio-inspired technique called gene expression programming (GEP) is integrated with artificial immune system (AIS) in detector representation. In addition, inspired by the avidity model of immunology, this paper also defines new avidity/affinity functions (corresponding to the metric for quantify the interactions between detector and antigens) that take the priorities of attribute into account. Accordingly, the proposed algorithm integrates both negative selection and positive selection with a balance factor k to assign appropriate weights to self and non-self avidity. The well known KDD CUP'99 DATA set is used for performance evaluation. The results show that the intrusion detection based on AMCS provides a higher detection rate of DoS attack, a lower false alarm rate, and a lower detectors generation cost. Our results indicate that breaking the bottleneck of immune-inspired network IDS through adjusting basic elements is feasible and effective.
Cited by 7
Nor Aishah Mustapa · UiTM Institutional Repositories (Universiti Teknologi MARA) · 2012
Diabetes Prediction system using clonal selection algorithm is available to test whether patients suffering from diabetes or not. The data included in the system is a tested and accurate data. Previously, the process of testing a patient suffering from diabetes tested manually. Results received by the patient are very slow and ineffective. Therefore, this system is established to help the hospital to reduce the time taken for each patient knows their decision. There are many techniques that can be used to make prediction process but the technique that has been selected to be used for this system is Artificial Immune System. Artificial Immune System has three types of selection that can be used such as clonal selection, network selection and negative selection. The prediction is done using Artificial Immune System technique where some of applicant’s information is taken to predict and see whether they are predicted having diabetes or not. The first purpose for this prototype is to create or make prediction of some data to measure the accuracy rate that either with similar accuracy or better than manual process before this. The result achieved after using this system may make the accuracy of prediction diabetes improved. The percentages of actual output same with the desired output is more than 80%. In the Artificial Immune System training, testing data is repeatedly done for the purpose to get the better memory cell value where it will be used to predict the future data.
Cited by 0
Hejun Tong, Dongmei Fu, Dong Lin et al. · 2014
Due to the limitations of infrared instrument sampling window size and shooting distance, collection of largescale target often need multiple filming and multiple infrared image stitching. Since infrared image blur itself, the image stitching process exists the difficulty of feature points extraction and matching points confirmation. Faced with this problem we proposed an image stitching algorithm based on immune memory clonal selection. We improved Susan algorithm to extract feature points, and got matching points by the proposed algorithm. Simulation results show that the algorithm can obtain a high rate of matching points, so as to achieve a good performance.
Cited by 1Open access
Mengling Zhao, Yuan Zichun -, Liu Hongwei · International Journal of Advancements in Computing Technology · 2013
Cited by 0