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A novel clonal selection algorithm based fragile watermarking method

6th International Conference on Artificial Immune Systems

作者： Aslantas, Veysel Ozer, Saban Ozturk, Serkan Erciyes Univ Fac Engn Comp Engn Div TR-38039 Kayseri Turkey Erciyes Univ Fac Engn Elect & Elect Engn Div TR-38039 Kayseri Turkey

ISBN: (纸本)9783540739210

In this paper, a novel fragile watermarking method based on clonal selection algorithm (CSA), clonalG, is presented. In Discrete Cosine Transform (DCT) based fragile watermarking techniques, there occurs some degree of rounding errors because of the conversion of real numbers into integers in the process of transformation of image from frequency domain to spatial domain. In this paper, the rounding errors caused by this transformation process are corrected by using clonalG. Simulation results show that extracted watermark is obtained exactly the same as embedded watermark and optimum watermarked image transparency is achieved. In addition, the performance comparison of clonalG and genetic algorithm (GA) based methods is realized.

关键词： fragile image watermarking discrete cosine transform clonal selection algorithm multimedia

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On discrete models and immunological algorithms for protein structure prediction

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NATURAL COMPUTING 2011年第1期10卷 91-102页

作者： Cutello, Vincenzo Morelli, Giuseppe Nicosia, Giuseppe Pavone, Mario Scollo, Giuseppe Univ Catania Dept Math & Comp Sci Catania Italy

Discrete models for protein structure prediction embed the protein amino acid sequence into a discrete spatial structure, usually a lattice, where an optimal tertiary structure is predicted on the basis of simple assumptions relating to the hydrophobic-hydrophilic character of amino acids in the sequence and to relevant interactions for free energy minimization. While the prediction problem is known to be NP complete even in the simple setting of Dill's model with a 2D-lattice, a variety of bio-inspired algorithms for this problem have been proposed in the literature. Immunological algorithms are inspired by the kind of optimization that immune systems perform when identifying and promoting the replication of the most effective antibodies against given antigens. A quick, state-of-the-art survey of discrete models and immunological algorithms for protein structure prediction is presented in this paper, and the main design and performance features of an immunological algorithm for this problem are illustrated in a tutorial fashion.

关键词： Artificial immune system clonal selection algorithm Dill model Evolutionary algorithm Functional model protein HP model Immunological algorithm Protein folding Protein structure prediction

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An immune-inspired approach to qualitative system identification of biological pathways

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NATURAL COMPUTING 2011年第1期10卷 189-207页

作者： Pang, Wei Coghill, George M. Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Univ Aberdeen Dept Comp Sci Aberdeen AB24 3UE Scotland

In this paper, a special-purpose qualitative model learning (QML) system using an immune-inspired algorithm is proposed to qualitatively reconstruct biological pathways. We choose a real-world application, the detoxification pathway of Methylglyoxal (MG), as a case study. First a converter is implemented to convert possible pathways to qualitative models. Then a general learning strategy is presented. To improve the scalability of the proposed QML system and make it adapt to future more complicated pathways, a modified clonal selection algorithm (clonalG) is employed as the search strategy. The performance of this immune-inspired approach is compared with those of exhaustive search and two backtracking algorithms. The experimental results indicate that this immune-inspired approach can significantly improve the search efficiency when dealing with some complicated pathways with large-scale search spaces.

关键词： clonal selection algorithm Immune-inspired algorithm Pathway reconstruction Qualitative differential equation Qualitative model learning Qualitative reasoning Qualitative simulation

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Immune-inspired Quantum Genetic Optimization algorithm and Its Application

Immune-inspired Quantum Genetic Optimization Algorithm and I...

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International Conference on Smart Materials and Intelligent Systems

作者： Zhe, Lian Shenyang Ligong Univ Ctr Comp Shenyang Peoples R China

ISBN: (纸本)9780878492237

Artificial immune systems (AIS), inspired by the natural immune systems, are an emerging kind of soft computing methods. This paper brings forward an immune-inspired quantum genetic optimization algorithm (IQGOA) based on clonal selection algorithm. The IQGOA is an evolutionary computation method inspired by the immune clonal principle of human immune system. To show the versatility and flexibility of the proposed IQGOA, some examples are given. Experimental results have shown that IQGOA is superior to clonal selection algorithm and Genetic algorithm (GA) on performance.

关键词： Genetic algorithm clonal selection algorithm quantum algorithm immune-inspired quantum genetic optimization algorithm

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An immune-inspired approach to qualitative system identification of biological pathways

An immune-inspired approach to qualitative system identifica...

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15th International Conference on DNA Computing and Molecular Programming

作者： Pang, Wei Coghill, George M. Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Univ Aberdeen Dept Comp Sci Aberdeen AB24 3UE Scotland

关键词： clonal selection algorithm Immune-inspired algorithm Pathway reconstruction Qualitative differential equation Qualitative model learning Qualitative reasoning Qualitative simulation

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Immune algorithm with orthogonal design based initialization, cloning, and selection for global optimization

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KNOWLEDGE AND INFORMATION SYSTEMS 2010年第3期25卷 523-549页

作者： Gong, Maoguo Jiao, Licheng Liu, Fang Ma, Wenping Xidian Univ Inst Intelligent Informat Proc Minist Educ China Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

In this study, an orthogonal immune algorithm (OIA) is proposed for global optimization by incorporating orthogonal initialization, a novel neighborhood orthogonal cloning operator, a static hypermutation operator, and a novel diversity-based selection operator. The orthogonal initialization scans the feasible solution space once to locate good points for further exploration in subsequent iterations. Meanwhile, each row of the orthogonal array defines a sub-domain. The neighborhood orthogonal cloning operator uses orthogonal arrays to scan uniformly the neighborhood around each antibody. Then the new algorithm explores each clone by using hypermutation. The improved maturated progenies are selectively added to an external population by the diversity-based selection, which retains one and only one external antibody in each sub-domain. The OIA is unique in three aspects: First, a new selection method based on orthogonal arrays is provided in order to preserve diversity in the population. Second, the orthogonal design with a modified quantization technique is introduced to generate initial population. Third, the orthogonal design is introduced into the cloning operator. The performance comparisons of OIA with two known immune algorithms and three evolutionary algorithms in optimizing eight benchmark functions and six composition functions indicate that OIA is an effective algorithm for solving global optimization problems.

关键词： clonal selection algorithm Evolutionary algorithm Orthogonal design Global optimization

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A hybrid particle swarm optimization for parallel machine total tardiness scheduling

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2010年第5-8期49卷 723-739页

作者： Niu, Qun Zhou, Taijin Wang, Ling Shanghai Univ Sch Mechatron Engn & Automat Shanghai Key Lab Power Stn Shanghai Peoples R China

The parallel machine scheduling problem has received increasing attention in recent years. This research considers the problem of scheduling jobs on parallel machines with a total tardiness objective. In the view of its non-deterministic polynomial-time hard nature, the particle swarm optimization (PSO), which is inspired by the swarming or collaborative behavior of biological populations, is employed to solve the parallel machine total tardiness problem (PMTP). Since it is very hard to directly apply standard PSO to this problem, a new solution representation is designed based on real number encoding, which can conveniently convert the job sequences of PMTP to continuous position values. Moreover, in order to enhance the performance of PSO, we introduce clonal selection algorithm (CSA) into PSO and therefore propose a new CSPSO method. The incorporation of CSA can greatly improve the swarm diversity and avoid premature convergence. We further investigate three parameters of PSO and CSPSO, finding that the parameters have marginal impact on CSPSO, which indicates that CSPSO is a very stable and robust method. The performance of CSPSO is evaluated in comparison with traditional genetic algorithm (GA) and standard PSO on 250 benchmark instances. Experimental results show that CSPSO significantly outperforms GA and PSO, with obtaining the optimal solutions of 237 instances. Additionally, PSO appears more effective than GA.

关键词： Parallel machine scheduling Particle swarm optimization clonal selection algorithm Total tardiness

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An Improved Artificial Immune Approach to Network Intrusion Detection

An Improved Artificial Immune Approach to Network Intrusion ...

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2nd IEEE International Conference on Advanced Computer Control

作者： Fang, Xianjin Li, Longshu Anhui Univ Sci & Technol Sch Comp Sci & Engn Huainan 232001 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230039 Peoples R China

ISBN: (纸本)9781424458479

The definition of vaccine, vaccine autonomous obtaining algorithm, vaccination operator and evolution of vaccine library are proposed in this paper. The generic clonal selection algorithm is improved by integrating with vaccine autonomous obtaining and vaccination operator, whose purpose is to accelerate the convergence speed of clonal selection algorithm, and improve the efficiency of the algorithm. The improved clonal selection algorithm together with negative selection mechanism in immunology is used to create the model of network intrusion detection. Simulated experimental results and theoretical computation efficiency analyses demonstrate the availability of the model and the speedup of convergence.

关键词： Artificial Immune System clonal selection algorithm Vaccination operator network Intrusion Detection

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A New Artificial Immune algorithm for Flexible Job-shop Scheduling

A New Artificial Immune Algorithm for Flexible Job-shop Sche...

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International Conference on Advances in Computer Science and Engineering

作者： Hong Lu Huaihai Inst Technol Dept Elect Engn Lianyungang Peoples R China

ISBN: (纸本)9780878492510

Flexible job-sop scheduling problem (FJSP) is based on the classical job-shop scheduling problem (JSP). however, it is even harder than JSP because of the addition of machine selection process in FJSP. An improved artificial immune algorithm, which combines the stretching technique and clonal selection algorithm is proposed to solve the FJSP. The algorithm can keep workload balance among the machines, improve the quality of the initial population and accelerate the speed of the algorithm's convergence. The details of implementation for the multi-objective FJSP and the corresponding computational experiments are reported. The results indicate that the proposed algorithm is an efficient approach for the multi-objective FJSP.

关键词： Artificial immune system clonal selection algorithm Stretching technique Flexible job shop scheduling problem

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Recognition of handwritten indic script using clonal selection algorithm

Recognition of handwritten indic script using clonal selecti...

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5th International Conference on Artificial Immune Systems

作者： Garain, Utpal Chakraborty, Mangal P. Dasgupta, Dipankar Indian Stat Inst Kolkata 700108 India Univ Memphis Memphis TN 38152 USA

ISBN: (纸本)3540377492

The work explores the potentiality of a clonal selection algorithm in pattern recognition (PR). In particular, a retraining scheme for the clonal selection algorithm is formulated for better recognition of handwritten numerals (a 10-class classification problem). Empirical study with two datasets (each of which contains about 12,000 handwritten samples for 10 numerals) shows that the proposed approach exhibits very good generalization ability. Experimental results reported the average recognition accuracy of about 96%. The effect of control parameters on the performance of the algorithm is analyzed and the scope for further improvement in recognition accuracy is discussed.

关键词： clonal selection algorithm character recognition Indic scripts handwritten digits

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