Harmony Search (HS) is a metaheuristic optimisation algorithm inspired by musical improvisation. So far it has been applied to various optimisation problems, and there are several application-oriented review papers. H...
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Harmony Search (HS) is a metaheuristic optimisation algorithm inspired by musical improvisation. So far it has been applied to various optimisation problems, and there are several application-oriented review papers. However, this review paper tries to focus on the historical development of algorithm structure instead of applications. This paper explains the original HS algorithm along with a selection of modified and hybrid HS methods: adaption of original operators of the basic harmony search, parameter adaption, hybrid methods, handling multi objective optimisation problems and constraint handling.
The fractal art graphic is one of the main manifestations of fractal art, which can be produced through mathematical models and programming on a computer. The paper investigates the designing concept of fractal art ba...
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ISBN:
(纸本)9783037850978
The fractal art graphic is one of the main manifestations of fractal art, which can be produced through mathematical models and programming on a computer. The paper investigates the designing concept of fractal art based on its self similarity and the iterative method, elaborates in detail the algorithms and steps of several kind of typical fractal graphics, and by properly inserting some controlling variables, has generated a large number of exquisite and inspiring fractal graphics using the JAVA programming language, confirming the validity and usability of presented algorithm's.
The fractal art graphic is one of the main manifestations of fractal art,which can be produced through mathematical models and programming on a computerThe paper investigates the designing concept of fractal art based...
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The fractal art graphic is one of the main manifestations of fractal art,which can be produced through mathematical models and programming on a computerThe paper investigates the designing concept of fractal art based on its self similarity and the iterative method,elaborates in detail the algorithms and steps of several kind of typical fractal graphics,and by properly inserting some controlling variables,has generated a large number of exquisite and inspiring fractal graphics using the JAVA programming language,confirming the validity and usability of presented algorithm's
Frequent subgraph mining(FSM) is a subset of the graph mining domain that is extensively used for graph classification and clustering. Over the past decade, many efficient FSM algorithms have been developed with impro...
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Frequent subgraph mining(FSM) is a subset of the graph mining domain that is extensively used for graph classification and clustering. Over the past decade, many efficient FSM algorithms have been developed with improvements generally focused on reducing the time complexity by changing the algorithm structure or using parallel programming techniques. FSM algorithms also require high memory consumption, which is another problem that should be solved. In this paper, we propose a new approach called Predictive dynamic sized structure packing(PDSSP) to minimize the memory needs of FSM algorithms. Our approach redesigns the internal data structures of FSM algorithms without making algorithmic modifications. PDSSP offers two contributions. The first is the Dynamic Sized Integer Type, a newly designed unsigned integer data type, and the second is a data structure packing technique to change the behavior of the compiler. We examined the effectiveness and efficiency of the PDSSP approach by experimentally embedding it into two state-of-the-art algorithms, g Span and *** compared our implementations to the performance of the originals. Nearly all results show that our proposed implementation consumes less memory at each support level, suggesting that PDSSP extensions could save memory, with peak memory usage decreasing up to 38% depending on the dataset.
A theory of the structural composition of an alilorithm is presented which allows the frequencies of occurrence of the individual operators and operands to be estimated. It provides justification for some recent hypot...
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In this paper, a distributed PSO algorithm in the JADE platform is studied based on traditional PSO. A parallel PSO algorithm structure based on Multi-agent corporative is proposed. The structure is made tip of severa...
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ISBN:
(纸本)9780769533575
In this paper, a distributed PSO algorithm in the JADE platform is studied based on traditional PSO. A parallel PSO algorithm structure based on Multi-agent corporative is proposed. The structure is made tip of several compute units. Each unit is a compute agent running basic particles swarm optimization. Simulation results show that the distributed structure can enhance system running efficiency. With the principal and subordinate running mechanism, the communication step is simplified, the running efficiency is optimized and the realization speed is enhanced.
Convolutional neural network(CNN) was a widely used algorithm for image classification in the field of computer *** present,in terms of identification of illegal web pages,the main application methods rely on manpower...
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ISBN:
(纸本)9781510871076
Convolutional neural network(CNN) was a widely used algorithm for image classification in the field of computer *** present,in terms of identification of illegal web pages,the main application methods rely on manpower too much,which is a costly and time-consuming *** paper will apply the CNN algorithm to the identification of illegal networks,build a CNN algorithm framework on the server side of browsers and web *** can also use CNN's outstanding performance in image classification to classify images of illegal websites and conducts realtime data on illegal *** paper will introduce the CNN algorithm characteristics from the algorithm structure and function.
Finding network communities (i.e. community detection) is a famous topic in network science. By far, many widely concerned community detection approaches are designed by using evolutionary computation methods. Recent ...
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ISBN:
(数字)9781728158570
ISBN:
(纸本)9781728158587
Finding network communities (i.e. community detection) is a famous topic in network science. By far, many widely concerned community detection approaches are designed by using evolutionary computation methods. Recent years, a new evolutionary algorithm called state transition algorithm (STA) was created and developed. In our previous work, a population-based discrete STA (MDSTA) has been put forwarded to settle network community detection task. Similar to most population-based evolutionary algorithms, MDSTA has a relatively complex algorithm structure which may limit the application of the algorithm. To address this problem, a backtracking-based discrete STA (BDSTA) is designed in this study. BDSTA is an individual-based method, and two kinds of substitute operators based on label-based representation strategy and locus-based representation strategy are used in BDSTA for global search and local search, respectively. Owing to that the individual-based algorithms often fall into a stagnation solution, we employ a backtracking search strategy in the global search procedure. Finally, five real-world networks and the extended GN artificial networks are used to test BDSTA and some state-of-art algorithms. Experimental results prove that BDSTA often get high-quality community partitions and it is more efficient than these state-of-art algorithms.
Diversity is deemed to be a key issue in classifier combination. For this reason, not every classifier is an expert for every query pattern. Thus, many researchers have focused on dynamic ensemble selection. Most work...
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ISBN:
(纸本)9781479919611
Diversity is deemed to be a key issue in classifier combination. For this reason, not every classifier is an expert for every query pattern. Thus, many researchers have focused on dynamic ensemble selection. Most works, however, use only one criterion to perform the dynamic selection. Hence, multiple criteria can provide a decision more effective than the one produced by any of the criteria. Another important issue is accuracy of the classifiers, which strongly depends on the adequate choice of its parameters, including, for example, learning algorithm, structure and input feature vector. Therefore, we present a hybrid intelligent system to generate automatically a pool of classifiers, and choose dynamically an ensemble to predict each query pattern. The method evolves simultaneously the classifier parameters and trains, via a learning algorithm, the candidate solutions. Meta-features are extracted and used to build meta-classifiers to predict whether a base classifier is competent enough to classify the query pattern. Experimental results show that the proposed method improves classification accuracy when compared against current state-of-the-art techniques.
Many real-world networks are described by both connectivity information and features for every node. To better model and understand these networks, we present structure preserving metric learning (SPML), an algorithm ...
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ISBN:
(纸本)9781618395993
Many real-world networks are described by both connectivity information and features for every node. To better model and understand these networks, we present structure preserving metric learning (SPML), an algorithm for learning a Mahalanobis distance metric from a network such that the learned distances are tied to the inherent connectivity structure of the network. Like the graph embedding algorithm structure preserving embedding, SPML learns a metric which is structure preserving, meaning a connectivity algorithm such as k-nearest neighbors will yield the correct connectivity when applied using the distances from the learned metric. We show a variety of synthetic and real-world experiments where SPML predicts link patterns from node features more accurately than standard techniques. We further demonstrate a method for optimizing SPML based on stochastic gradient descent which removes the running-time dependency on the size of the network and allows the method to easily scale to networks of thousands of nodes and millions of edges.
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