The Experts below are selected from a list of 19602 Experts worldwide ranked by ideXlab platform
Licheng Jiao - One of the best experts on this subject based on the ideXlab platform.
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hyperspectral band Selection based on trivariate mutual information and Clonal Selection
IEEE Transactions on Geoscience and Remote Sensing, 2014Co-Authors: Jie Feng, Licheng Jiao, Xiangrong Zhang, Tao SunAbstract:Band Selection is an important preprocessing step for hyperspectral data processing. It involves two crucial problems, i.e., suitable measure criterion and effective search strategy. Mutual information (MI) has been widely used as the measure criterion for its nonlinear and nonparametric characteristics. For efficient calculation, traditional MI-based criteria commonly use bivariate MI (BMI) to approximate the ideal MI-based criterion. However, these BMI-based criteria may miss the bands having discriminative information and do not give the condition of the approximation. In this paper, a novel criterion based on trivariate MI (TMI) is proposed to measure the redundancy for classification. From the multivariate MI perspective, the proposed TMI-based and traditional BMI-based criteria are proved as the low-order approximations of the ideal criterion under some assumptions. Compared with the BMI-based criteria, a more relaxed assumption condition is required for the TMI-based criterion. To alleviate the problem of few labeled samples existing in hyperspectral images, the TMI-based criterion is extended to the semisupervised TMI-based (STMI) method by adding a graph regulation term. Additionally, to search an appropriate band subset by the TMI- and STMI-based criteria, a new Clonal Selection algorithm (CSA) is proposed. In CSA, integer encoding and adaptive operators are devised to reduce space and time cost. Experimental results demonstrate the effectiveness of the proposed algorithms for hyperspectral band Selection.
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bag of visual words based on Clonal Selection algorithm for sar image classification
IEEE Geoscience and Remote Sensing Letters, 2011Co-Authors: Jie Feng, Licheng Jiao, Xiangrong Zhang, Dongdong YangAbstract:Synthetic aperture radar (SAR) image classification involves two crucial issues: suitable feature representation technique and effective pattern classification methodology. Here, we concentrate on the first issue. By exploiting a famous image feature processing strategy, Bag-of-Visual-Words (BOV) in image semantic analysis and the artificial immune systems (AIS)'s abilities of learning and adaptability to solve complicated problems, we present a novel and effective image representation method for SAR image classification. In BOV, an effective fused feature sets for local feature representation are first formulated, which are viewed as the low-level features in it. After that, Clonal Selection algorithm (CSA) in AIS is introduced to optimize the prediction error of k-fold cross-validation for getting more suitable visual words from the low-level features. Finally, the BOV features are represented by the learned visual words for subsequent pattern classification. Compared with the other four algorithms, the proposed algorithm obtains more satisfactory and cogent classification experimental results.
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baldwinian learning in Clonal Selection algorithm for optimization
Information Sciences, 2010Co-Authors: Maoguo Gong, Licheng Jiao, Lining ZhangAbstract:Artificial immune systems are a kind of new computational intelligence methods which draw inspiration from the human immune system. Most immune system inspired optimization algorithms are based on the applications of Clonal Selection and hypermutation, and known as Clonal Selection algorithms. These Clonal Selection algorithms simulate the immune response process based on principles of Darwinian evolution by using various forms of hypermutation as variation operators. The generation of new individuals is a form of the trial and error process. It seems very wasteful not to make use of the Baldwin effect in immune system to direct the genotypic changes. In this paper, based on the Baldwin effect, an improved Clonal Selection algorithm, Baldwinian Clonal Selection Algorithm, termed as BCSA, is proposed to deal with optimization problems. BCSA evolves and improves antibody population by four operators, Clonal proliferation, Baldwinian learning, hypermutation, and Clonal Selection. It is the first time to introduce the Baldwinian learning into artificial immune systems. The Baldwinian learning operator simulates the learning mechanism in immune system by employing information from within the antibody population to alter the search space. It makes use of the exploration performed by the phenotype to facilitate the evolutionary search for good genotypes. In order to validate the effectiveness of BCSA, eight benchmark functions, six rotated functions, six composition functions and a real-world problem, optimal approximation of linear systems are solved by BCSA, successively. Experimental results indicate that BCSA performs very well in solving most of the test problems and is an effective and robust algorithm for optimization.
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directional filter for sar images based on nonsubsampled contourlet transform and immune Clonal Selection
International Journal of Automation and Computing, 2009Co-Authors: Licheng Jiao, Xiaohui Yang, Dengfeng LiAbstract:A directional filter algorithm for intensity synthetic aperture radar (SAR) image based on nonsubsampled contourlet transform (NSCT) and immune Clonal Selection (ICS) is presented. The proposed filter mainly focuses on exploiting different features of edges and noises by NSCT. Furthermore, ICS strategy is introduced to optimize threshold parameter and amplify parameter adaptively. Numerical experiments on real SAR images show that there are improvements in both visual effects and objective indexes.
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immune secondary response and Clonal Selection inspired optimizers
Progress in Natural Science, 2009Co-Authors: Maoguo Gong, Licheng Jiao, Lining Zhang, Haifeng DuAbstract:Abstract The immune system’s ability to adapt its B cells to new types of antigen is powered by processes known as Clonal Selection and affinity maturation. When the body is exposed to the same antigen, immune system usually calls for a more rapid and larger response to the antigen, where B cells have the function of negative adjustment. Based on the Clonal Selection theory and the dynamic process of immune response, two novel artificial immune system algorithms, secondary response Clonal programming algorithm (SRCPA) and secondary response Clonal multi-objective algorithm (SRCMOA), are presented for solving single and multi-objective optimization problems, respectively. Clonal Selection operator (CSO) and secondary response operator (SRO) are the main operators of SRCPA and SRCMOA. Inspired by the Clonal Selection theory, CSO reproduces individuals and selects their improved maturated progenies after the affinity maturation process. SRO copies certain antibodies to a secondary pool, whose members do not participate in CSO, but these antibodies could be activated by some external stimulations. The update of the secondary pool pays more attention to maintain the population diversity. On the one hand, decimal-string representation makes SRCPA more suitable for solving high-dimensional function optimization problems. Special mutation and recombination methods are adopted in SRCPA to simulate the somatic mutation and receptor editing process. Compared with some existing evolutionary algorithms, such as OGA/Q, IEA, IMCPA, BGA and AEA, SRCPA is shown to be able to solve complex optimization problems, such as high-dimensional function optimizations, with better performance. On the other hand, SRCMOA combines the Pareto-strength based fitness assignment strategy, CSO and SRO to solve multi-objective optimization problems. The performance comparison between SRCMOA, NSGA-II, SPEA, and PAES based on eight well-known test problems shows that SRCMOA has better performance in converging to approximate Pareto-optimal fronts with wide distributions.
Peter J Bentley - One of the best experts on this subject based on the ideXlab platform.
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towards an artificial immune system for network intrusion detection an investigation of dynamic Clonal Selection
Congress on Evolutionary Computation, 2002Co-Authors: Jungwon Kim, Peter J BentleyAbstract:One significant feature of artificial immune systems is their ability to adapt to continuously changing environments, dynamically learning the fluid patterns of 'self' and predicting new patterns of 'non-self'. This paper introduces and investigates the behaviour of dynamiCS, a dynamic Clonal Selection algorithm, designed to have such properties of self-adaptation. The effects of three important system parameters: tolerisation period, activation threshold, and life span are explored. The abilities of dynamiCS to perform incremental learning on converged data, and to adapt to novel data are also demonstrated.
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towards an artificial immune system for network intrusion detection an investigation of Clonal Selection with a negative Selection operator
Congress on Evolutionary Computation, 2001Co-Authors: Jungwon Kim, Peter J BentleyAbstract: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.
Jungwon Kim - One of the best experts on this subject based on the ideXlab platform.
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towards an artificial immune system for network intrusion detection an investigation of dynamic Clonal Selection
Congress on Evolutionary Computation, 2002Co-Authors: Jungwon Kim, Peter J BentleyAbstract:One significant feature of artificial immune systems is their ability to adapt to continuously changing environments, dynamically learning the fluid patterns of 'self' and predicting new patterns of 'non-self'. This paper introduces and investigates the behaviour of dynamiCS, a dynamic Clonal Selection algorithm, designed to have such properties of self-adaptation. The effects of three important system parameters: tolerisation period, activation threshold, and life span are explored. The abilities of dynamiCS to perform incremental learning on converged data, and to adapt to novel data are also demonstrated.
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towards an artificial immune system for network intrusion detection an investigation of Clonal Selection with a negative Selection operator
Congress on Evolutionary Computation, 2001Co-Authors: Jungwon Kim, Peter J BentleyAbstract: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.
Maoguo Gong - One of the best experts on this subject based on the ideXlab platform.
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baldwinian learning in Clonal Selection algorithm for optimization
Information Sciences, 2010Co-Authors: Maoguo Gong, Licheng Jiao, Lining ZhangAbstract:Artificial immune systems are a kind of new computational intelligence methods which draw inspiration from the human immune system. Most immune system inspired optimization algorithms are based on the applications of Clonal Selection and hypermutation, and known as Clonal Selection algorithms. These Clonal Selection algorithms simulate the immune response process based on principles of Darwinian evolution by using various forms of hypermutation as variation operators. The generation of new individuals is a form of the trial and error process. It seems very wasteful not to make use of the Baldwin effect in immune system to direct the genotypic changes. In this paper, based on the Baldwin effect, an improved Clonal Selection algorithm, Baldwinian Clonal Selection Algorithm, termed as BCSA, is proposed to deal with optimization problems. BCSA evolves and improves antibody population by four operators, Clonal proliferation, Baldwinian learning, hypermutation, and Clonal Selection. It is the first time to introduce the Baldwinian learning into artificial immune systems. The Baldwinian learning operator simulates the learning mechanism in immune system by employing information from within the antibody population to alter the search space. It makes use of the exploration performed by the phenotype to facilitate the evolutionary search for good genotypes. In order to validate the effectiveness of BCSA, eight benchmark functions, six rotated functions, six composition functions and a real-world problem, optimal approximation of linear systems are solved by BCSA, successively. Experimental results indicate that BCSA performs very well in solving most of the test problems and is an effective and robust algorithm for optimization.
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immune secondary response and Clonal Selection inspired optimizers
Progress in Natural Science, 2009Co-Authors: Maoguo Gong, Licheng Jiao, Lining Zhang, Haifeng DuAbstract:Abstract The immune system’s ability to adapt its B cells to new types of antigen is powered by processes known as Clonal Selection and affinity maturation. When the body is exposed to the same antigen, immune system usually calls for a more rapid and larger response to the antigen, where B cells have the function of negative adjustment. Based on the Clonal Selection theory and the dynamic process of immune response, two novel artificial immune system algorithms, secondary response Clonal programming algorithm (SRCPA) and secondary response Clonal multi-objective algorithm (SRCMOA), are presented for solving single and multi-objective optimization problems, respectively. Clonal Selection operator (CSO) and secondary response operator (SRO) are the main operators of SRCPA and SRCMOA. Inspired by the Clonal Selection theory, CSO reproduces individuals and selects their improved maturated progenies after the affinity maturation process. SRO copies certain antibodies to a secondary pool, whose members do not participate in CSO, but these antibodies could be activated by some external stimulations. The update of the secondary pool pays more attention to maintain the population diversity. On the one hand, decimal-string representation makes SRCPA more suitable for solving high-dimensional function optimization problems. Special mutation and recombination methods are adopted in SRCPA to simulate the somatic mutation and receptor editing process. Compared with some existing evolutionary algorithms, such as OGA/Q, IEA, IMCPA, BGA and AEA, SRCPA is shown to be able to solve complex optimization problems, such as high-dimensional function optimizations, with better performance. On the other hand, SRCMOA combines the Pareto-strength based fitness assignment strategy, CSO and SRO to solve multi-objective optimization problems. The performance comparison between SRCMOA, NSGA-II, SPEA, and PAES based on eight well-known test problems shows that SRCMOA has better performance in converging to approximate Pareto-optimal fronts with wide distributions.
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Clonal Selection algorithm with immunologic regulation for function optimization
Computational Intelligence and Security, 2005Co-Authors: Hang Yu, Maoguo Gong, Licheng Jiao, Bin ZhangAbstract:Based on the Antibody Clonal Selection Theory of immunology, four immunologic regulation operators inspired by immune regulation mechanism of biology immune system are presented in this paper, and a corresponding algorithm, Immunologic Regulation Clonal Selection Algorithm (IRCSA), is put forward. The essential of immunologic regulation operators is to make fine adjustment among the candidates of the algorithm so as to make interrelations between antibodies more complicated and improve the stability, robustness and accuracy of the algorithm. Numeric experiments of function optimization indicate that the new algorithm is effective and useful.
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Clonal Selection with immune dominance and anergy based multiobjective optimization
International Conference on Evolutionary Multi-criterion Optimization, 2005Co-Authors: Licheng Jiao, Maoguo Gong, Ronghua ShangAbstract:Based on the concept of Immunodominance and Antibody Clonal Selection Theory, we propose a new artificial immune system algorithm, Immune Dominance Clonal Multiobjective Algorithm (IDCMA). The influences of main parameters are analyzed empirically. The simulation comparisons among IDCMA, the Random-Weight Genetic Algorithm and the Strength Pareto Evolutionary Algorithm show that when low-dimensional multiobjective problems are concerned, IDCMA has the best performance in metrics such as Spacing and Coverage of Two Sets.
Thomas Stiehl - One of the best experts on this subject based on the ideXlab platform.
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A structured population model of Clonal Selection in acute leukemias with multiple maturation stages
Journal of Mathematical Biology, 2019Co-Authors: Tommaso Lorenzi, Anna Marciniak-czochra, Thomas StiehlAbstract:Recent progress in genetic techniques has shed light on the complex co-evolution of malignant cell clones in leukemias. However, several aspects of Clonal Selection still remain unclear. In this paper, we present a multi-compartmental continuously structured population model of Selection dynamics in acute leukemias, which consists of a system of coupled integro-differential equations. Our model can be analysed in a more efficient way than classical models formulated in terms of ordinary differential equations. Exploiting the analytical tractability of this model, we investigate how Clonal Selection is shaped by the self-renewal fraction and the proliferation rate of leukemic cells at different maturation stages. We integrate analytical results with numerical solutions of a calibrated version of the model based on real patient data. In summary, our mathematical results formalise the biological notion that Clonal Selection is driven by the self-renewal fraction of leukemic stem cells and the clones that possess the highest value of this parameter are ultimately selected. Moreover, we demonstrate that the self-renewal fraction and the proliferation rate of non-stem cells do not have a substantial impact on Clonal Selection. Taken together, our results indicate that interClonal variability in the self-renewal fraction of leukemic stem cells provides the necessary substrate for Clonal Selection to act upon.
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Clonal Selection and therapy resistance in acute leukaemias mathematical modelling explains different proliferation patterns at diagnosis and relapse
Journal of the Royal Society Interface, 2014Co-Authors: Thomas Stiehl, Natalia Baran, Anna MarciniakczochraAbstract:Recent experimental evidence suggests that acute myeloid leukaemias may originate from multiple clones of malignant cells. Nevertheless, it is not known how the observed clones may differ with respect to cell properties, such as proliferation and self-renewal. There are scarcely any data on how these cell properties change due to chemotherapy and relapse. We propose a new mathematical model to investigate the impact of cell properties on the multi-Clonal composition of leukaemias. Model results imply that enhanced self-renewal may be a key mechanism in the Clonal Selection process. Simulations suggest that fast proliferating and highly self-renewing cells dominate at primary diagnosis, while relapse following therapy-induced remission is triggered mostly by highly self-renewing but slowly proliferating cells. Comparison of simulation results to patient data demonstrates that the proposed model is consistent with clinically observed dynamics based on a Clonal Selection process.
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Clonal Selection and therapy resistance in acute leukemias mathematical modelling explains different proliferation patterns at diagnosis and relapse
arXiv: Tissues and Organs, 2013Co-Authors: Thomas Stiehl, Natalia Baran, Anna MarciniakczochraAbstract:Recent experimental evidence suggests that acute myeloid leukemias may originate from multiple clones of malignant cells. Nevertheless it is not known how the observed clones may differ with respect to cell properties such as proliferation and self-renewal. There are scarcely any data on how these cell properties change due to chemotherapy and relapse. We propose a new mathematical model to investigate the impact of cell properties on multi-Clonal composition of leukemias. Model results imply that enhanced self-renewal may be a key mechanism in the Clonal Selection process. Simulations suggest that fast proliferating and highly self-renewing cells dominate at primary diagnosis while relapse following therapy-induced remission is triggered mostly by highly self-renewing but slowly proliferating cells. Comparison of simulation results to patient data demonstrates that the proposed model is consistent with clinically observed dynamics based on a Clonal Selection process.