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检索条件"主题词=Clonal selection algorithm"

共 233 条记录，以下是61-70 订阅

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Solving DNA Motif Discovery Problem using Improved clonal selection algorithm with Tournament selection Operator

Solving DNA Motif Discovery Problem using Improved Clonal Se...

引用

1st International Conference on Advanced Information Science and System (AISS)

作者： Ulker, Ezgi Deniz European Univ Lefke Dept Comp Engn Mersin 10 Karavostasi Turkey

ISBN: (纸本)9781450372916

Identifying the regulatory elements in deoxyribonucleic acid (DNA) is a challenging area of research. The order of nucleotides in a DNA sequence determines the genetic functionality. The common nucleotides called motifs which have short sizes are placed in large number of sequences. Because of the difficulty to obtain the motifs among the large number of sequences, Motif Discovery Problem (MDP) is referred as a NP-hard problem. Since this problem is considered an open area for researchers, the evolutionary algorithms can be used to bring new solutions to this field. In this paper, Improved clonal selection algorithm with Tournament selection operator (ICSAT) is adapted to solve MDP by using real DNA sequences and compared with some other algorithms which are particularly designed to solve it. The results denote that ICSAT obtains motifs between the large sequences and it produces efficient results in terms of motif length and similarity values with respect to the compared algorithms. It can be used as a good candidate for solving these kinds of problems in bioinformatics.

关键词： Biological optimization clonal selection algorithm Motif discovery Tournament selection

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A clonal selection algorithm Based Fuzzy Optimal Iterative Learning Control algorithm

A Clonal Selection Algorithm Based Fuzzy Optimal Iterative L...

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2nd International Conference on Mechatronics and Intelligent Materials (MIM 2012)

作者： Li, Hengjie Hao, Xiaohong Du, Xianjun Jin, Yarong Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou 730050 Gansu Peoples R China

ISBN: (纸本)9783037853849

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.

关键词： clonal selection algorithm Iterative Learning Control TSK Fuzzy System

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Modified Elite Chaotic Immune clonal selection algorithm for Sever Resource Allocation in Cloud Computing Systems

Modified Elite Chaotic Immune Clonal Selection Algorithm for...

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International Symposium on Wireless Personal Multimedia Communications (WPMC)

作者： Zhou, Jie Dutkiewicz, Eryk Liu, Ren Ping Fang, Gengfa Liu, Yuanan Macquarie Univ Dept Engn Sydney NSW Australia Beijing Univ Post & Telecommun Sch Elect Engn Beijing Peoples R China CSIRO Canberra ACT Australia

ISBN: (纸本)9789860334074

Cloud computing is a promising technology to improve computational efficiency for both IT enterprise and individuals. Resource allocation in cloud computing is very challenging as both server computing power and network bandwidth are limited. The computational efficiency of cloud computing system can be significantly improved if the resources are allocated in a balanced fashion. However, resource allocation in cloud computing is a multi-constrained nonlinear optimization problem. The computational complexity for an exhaustive search over all combinations of resource allocations is too high for practical implementation. In this paper, we develop a Modified Elite Chaotic Immune clonal selection algorithm to increase the overall efficiency of the system. An elite strategy and chaotic approaches are designed to improve population diversity and escape from local optima. Performance comparisons are made with simulated annealing algorithm (SA) and three other heuristic algorithms. Simulation results show that the Modified Elite Chaotic Immune clonal selection algorithm solves the resource allocation problem with higher system resource efficiency than all other heuristic algorithms.

关键词： cloud computing resource allocation workload balance clonal selection algorithm

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A fast clonal selection algorithm for constructing an immune neural network

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11th Joint International Computer Conference (JICC 2005)

作者： Liu, HB Gu, GC Shen, J Yan, F Harbin Engn Univ Coll Comp Sci & Technol Harbin 150001 Peoples R China

ISBN: (纸本)9812565329

Unsupervised learning strategies such as self-organizing map (SOM) may be more fascinating in some applications in which lacks of supervised signals. But the standard SOM network has a fixed number of outputs that must be pre-specified before training, which lacks of flexibility. In this paper, a fast clonal selection algorithm (FCSA) for constructing an immune neural network (INN) is presented based on clonal selection principle. The INN is a two-layer network whose number of outputs is adaptable according to the task and the affinity threshold. The constructed INN is similar to the ABNET proposed by de Castro et al, but the constructing algorithm FCSA is remarkably simpler, faster, and more facile than that of ABNET, which can be demonstrated in the simulation experiments.

关键词： immune neural network clonal selection algorithm unsupervised learning

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A clonal selection algorithm Based Optimal Iterative Learning Control algorithm

A Clonal Selection Algorithm Based Optimal Iterative Learnin...

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7th World Congress on Intelligent Control and Automation

作者： Li, Hengjie Hao, Xiaohong Zhang, Lei Lanzhou Univ Technol Sch Elect & Informat Engn Lanzhou 730050 Gansu Peoples R China

ISBN: (纸本)9781424421138

Improved clonal selection algorithms were proposed as a method to implement optimal iterative learning control algorithms. The strength of the method is that it not only can cope with non-minimum phase plants and nonlinear plants even there are uncertainties in their models, but also can deal with constraints on input signals conveniently by a specially designed mutation operator. Simulations show that the convergence speed is satisfactory regardless of the nature of the plants and whether or not the models of the plants are precise.

关键词： clonal selection algorithm iterative learning control on-minimum phase plants nonlinear plants constraints

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A discrete clonal selection algorithm for filter-based local feature selection

A discrete clonal selection algorithm for filter-based local...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Wang, Yi Li, Tao Liu, Xiaojie Yao, Jian Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan 411201 Peoples R China Sichuan Univ Coll Cybers Chengdu 610065 Peoples R China

ISBN: (数字)9781665467087

ISBN: (纸本)9781665467087

Feature selection algorithms aim to improve the performance 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.

关键词： local feature selection clonal selection algorithm artificial immune system dimensionality reduction

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A clonal selection algorithm with Levenshtein Distance based Image Similarity in Multidimensional Subjective Tourist Information and Discovery of Cryptic Spots by Interactive GHSOM

A Clonal Selection Algorithm with Levenshtein Distance based...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Ichimura, Takumi Kamada, Shin Prefectural Univ Hiroshima Fac Management & Informat Syst Minami Ku 1-1-71 Ujina Higashi Hiroshima 7348559 Japan Prefectural Univ Hiroshima Grad Sch Comprehens Sci Res Minami Ku Hiroshima 7348559 Japan

ISBN: (纸本)9781479906529

Mobile Phone based Participatory Sensing (MPPS) system involves a community of users sending personal information and participating in autonomous sensing through their mobile phones. Sensed data can also be obtained from external sensing devices that can communicate wirelessly to the phone. Our developed tourist subjective data collection system with Android smartphone can determine the filtering rules to provide the important information of sightseeing spot. The rules are automatically generated by Interactive Growing Hierarchical SOM. However, the filtering rules related to photograph were not generated, because the extraction of the specified characteristics from images cannot be realized. We propose the effective method of the Levenshtein distance to deduce the spatial proximity of image viewpoints and thus determine the specified pattern in which images should be processed. To verify the proposed method, some experiments to classify the subjective data with images are executed by Interactive GHSOM and clonal selection algorithm with Immunological Memory Cells in this paper.

关键词： Levenshtein Distance clonal selection algorithm Image Analysis Immunological Memory Cells Growing Hierarchical SOM Interactive GHSOM Smartphone based Participatory Sensing System Tourist Informatics Knowledge Discovery

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Stretching Technique-based clonal selection algorithm for Flexible Job-shop Scheduling

Stretching Technique-based Clonal Selection Algorithm for Fl...

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International Conference on Computational Intelligence and Natural Computing

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

ISBN: (纸本)9780769536453

Flexible Job-shop Scheduling Problem (FJSP) is expanded from the traditional Job-shop Scheduling Problem (JSP), which possesses wider availability of machines for all the operations. The aim is to find an allocation for each operation and to define the sequence of operations on each machine so that the resulting schedule has a minimal completion time. This paper introduces a hybrid metaheuristic, the stretching technique-based immune algorithm, consisting of a combination of the stretching technique and clonal selection algorithm (CSA). The proposed method is used for solving the multi-objective FJSP. 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, especially for large scale problems.

关键词： immune systems clonal selection algorithm stretching technique flexible job shop scheduling problem

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Hybrid Crossover Based clonal selection algorithm and Its Applications 17th

Hybrid Crossover Based Clonal Selection Algorithm and Its Ap...

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17th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)

作者： Dai, Hongwei Yang, Yu Li, Cunhua Huaihai Inst Technol Sch Comp Engn Lianyungang 222005 Jiangsu Peoples R China

ISBN: (纸本)9783319462578;9783319462561

Hybridization is confirmed as an effective way of combining the best properties of different algorithms and achieving better performances. A framework of hybrid crossover is constructed and combined with clonal selection algorithm (CSA). The new crossover solutions are generated by the mutual influence of both high affinity and low affinity solutions. Simulation results based on the traveling salesman problems demonstrate the effectiveness of the hybridization.

关键词： Hybrid crossover clonal selection algorithm Traveling salesman problem Simulation

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Neural Network Ensemble Based on clonal selection algorithm for Sneak Circuit Analysis

Neural Network Ensemble Based on Clonal Selection Algorithm ...

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International Conference on Sensors, Mechatronics and Automation (ICSMA)

作者： Liu, Bingjie Lu, Wenzhong Ji, Haiyan Navy Submarine Acad Missile Dept Qingdao 266042 Peoples R China

ISBN: (纸本)9783038350170

The paper proposed a novel neural network ensemble algrithm (NNNEA) whose individual was generated by clonal selection algorithm. NNNEA can produced individuals of ensemble with better difference than other algrithm. NNNEA was used for predicting ciruit functions and finding sneak circuit. The inputs of NNNEA are states of switches, and the outputs are states of functional components. NNNEA predicted all possible functions of circuit. The sneak circuits can be discovered by comparing the predicted with designed functions. Although there are several limitations of NNNNEA, the results revealed that NNNNEA can exactly discover sneak circuits.

关键词： sneak circuit analysis neural network ensemble clonal selection algorithm generalization performance

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