Cluster analysis is a major method to study gene function and gene regulation information for there is a lack of prior knowledge for gene data. Many clustering methods existed at present usually need manual operat...
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ISBN:
(纸本)9781424468126;9780769540306
Cluster analysis is a major method to study gene function and gene regulation information for there is a lack of prior knowledge for gene data. Many clustering methods existed at present usually need manual operations or predetermined parameters, which are difficult for gene data. Besides, gene data possess their own characteristics, such as large scale, high-dimension, and noise. Therefore, a systematic clustering algorithm should be proposed to effectively deal with gene data. In this paper, a novel genetic programming (GP) clustering system for gene data based on hierarchical statistical model (HS-model) is proposed. And an appropriate fitness function is also proposed in this system. This clustering system can largely eliminate the infection of data scale and dimension. The proposed GP clustering system is applied to cluster the whole intact yeast gene data without dimensionality reduction. The experimental results indicate that the algorithm is highly efficient and can effectively deal with missing values in gene dataset
Evolutionary multimodal optimization has received considerable attention in the past decade. Most existing evolutionary multimodal optimization algorithms are designed to solve problems with relatively few global opti...
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ISBN:
(数字)9781728124858
ISBN:
(纸本)9781728124865
Evolutionary multimodal optimization has received considerable attention in the past decade. Most existing evolutionary multimodal optimization algorithms are designed to solve problems with relatively few global optima. However, in real-world applications, the problems can possess a lot of global optima (and sometimes acceptable local optima). Finding more global optima can help us learn more about their landscapes and distributions. However, solving these problems with limited computational resources is a challenge for current algorithms. In this paper, many-modal optimization problems are studied, and each of them has more than 100 global optima. We first present a benchmark with 10 many-modal problems based on the existing multimodal optimization benchmarks. The numbers of global optima of these 10 problems vary from 108 to 7776. Second, we propose the difficulty-based cooperative co-evolution (DBCC) strategy for solving many-modal optimization problems. DBCC comprises four primary steps: problem separation, resource allocation, optimization, and solution reconstruction. The clonal selection algorithm is selected as the optimizer in DBCC. Experimental results demonstrate that DBCC provides satisfactory performance.
Due to the inherent flaws of BGP protocol, the security of inter domain routing system has been regularly threatened. Since it is difficult to achieve collaborative defense against existing resources and strategies of...
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Recently, the characterization of community structures in complex networks has received a considerable amount of attentions. Effective identification of these communities or clusters is a general problem in the fi...
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ISBN:
(纸本)9781424468126;9780769540306
Recently, the characterization of community structures in complex networks has received a considerable amount of attentions. Effective identification of these communities or clusters is a general problem in the field of data mining. In this paper we present a /ast hierarchical agglomerative algorithm based on community closeness (FHACC) algorithm, for detecting community structure which is very efficient and faster than many other competing algorithms. FHACC tends to agglomerate such communities that share the most common vertices into larger ones. Its running time on a sparse network with n vertices and m edges is O(mk + mt), where k denotes the mean vertex degree, and t is the iteration times of community agglomeration in FHACC algorithm. The algorithm was tested on several real-world networks and proved to be high efficient and effective in community finding.
Evolutionary algorithms have been adopted to design logic circuits for many years. However, it takes too much time for evolutionary algorithms to generate circuits directly. Recently, the repair technique has been int...
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Evolutionary algorithms have been adopted to design logic circuits for many years. However, it takes too much time for evolutionary algorithms to generate circuits directly. Recently, the repair technique has been introduced into evolutionary design of the circuit to significantly decrease the time cost. And yet, the repair technique costs a lot of gate resource. In this paper, the evolutionary repair for evolutionary design of the combinational circuit is proposed, which generates the repair circuits with an evolutionary algorithm. The evolutionary repair could reduce the gate resource cost and does not spend much more time. The evolutionary repair is merged into the traditional evolutionary algorithm to form a novel evolutionary design algorithm, i.e. the erEDA. The experimental results demonstrate that the erEDA could balance the time cost and the gate resource consumption.
The detector generation algorithm is the core of a negative selection algorithm (NSA). In most previous work, the NSAs generate the detector set randomly, which cannot guarantee to obtain an efficient detector set. To...
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The detector generation algorithm is the core of a negative selection algorithm (NSA). In most previous work, the NSAs generate the detector set randomly, which cannot guarantee to obtain an efficient detector set. To generate an approximately optimal detector set, in this paper, a novel detector generation algorithm for the real-valued negative selection algorithm (RNSA) is proposed. The proposed algorithm, named as the EvoSeedRNSA, adopts a genetic algorithm to evolve the random seeds to obtain an optimized detector set. The experimental results demonstrate that the EvoSeedRNSA has a better performance.
Interpersonal relationship quality is pivotal in social and occupational contexts. Existing analysis of interpersonal relationships mostly rely on subjective self-reports, whereas objective quantification remains chal...
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Graph convolutional neural network (GCN) is a powerful deep learning framework for network data. However, variants of graph neural architectures can lead to drastically different performance on different tasks. Model ...
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Asymmetric group key agreement allows a group of users to negotiate a public encryption key that corresponds to several decryption keys, and each decryption key can only be computed by one group member. This novel not...
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Asymmetric group key agreement allows a group of users to negotiate a public encryption key that corresponds to several decryption keys, and each decryption key can only be computed by one group member. This novel notion ensures the confidentiality of communication among group members and allows any external entity to send messages to the group. However, the existing authenticated asymmetric group key agreement protocols are designed in identity-based cryptosystem or certificateless public key cryptosystem, which are not widely deployed. In this paper, we propose an efficient authenticated asymmetric group key agreement protocol. The protocol captures the security of secrecy, known-key security, key-compromise impersonation, unknown key-share and key control while being resistant to active attacks. The security of our protocol is reduced to the k-BDHE problem.
The memory scheme is one of the most widely employed techniques in Evolutionary Algorithms for solving dynamic optimization problems. The updating strategy is a key concern for the memory scheme. Unfortunately, the ex...
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The memory scheme is one of the most widely employed techniques in Evolutionary Algorithms for solving dynamic optimization problems. The updating strategy is a key concern for the memory scheme. Unfortunately, the existent memory updating strategies neglect the characteristics of the memory updating behaviors, and sometimes this could lead results against the original intention. In this paper, a novel updating strategy is proposed, which can adaptively update the memory according to the characteristics of the memory updating behaviors. Experiments are carried out in different kinds of dynamic environments, and the experimental results show that the proposed strategy is better than the traditional strategies.
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