Learning from imbalanced datasets is a critical challenge confronting researchers. Unequal distribution of classes in the imbalanced datasets lead to biased classification especially in microarray gene expression anal...
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Learning from imbalanced datasets is a critical challenge confronting researchers. Unequal distribution of classes in the imbalanced datasets lead to biased classification especially in microarray gene expression analysis. Since all features in the dataset will not contribute to the analysis, only prominent and significant features need to be identified. The paper addresses both these issues by proposing wrapper based incremental genetic algorithm (IGA) which incrementally evaluates and adds attributes into the geneticalgorithm process rather than evaluation of all attributes thereby reducing the computational complexity and number of features used and improving the measures like classification accuracy, GMean, F1 measure, precision and recall. The experiments are conducted on 8 microarray gene expression datasets and the results show that performance of IGA is encouraging and superior to existing methods that are compared.
Graph pattern matching is a key problem in many applications which data is represented in the form of a graph, and this problem is generally defined as a subgraph isomorphism. In this paper, we analyze an incremental ...
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Graph pattern matching is a key problem in many applications which data is represented in the form of a graph, and this problem is generally defined as a subgraph isomorphism. In this paper, we analyze an incremental hybrid geneticalgorithm for the subgraph isomorphism problem considering various design issues to improve the performance of the algorithm. An incremental hybrid geneticalgorithm was previously suggested to solve the subgraph isomorphism problem and have shown good performance. It decomposes the problem into a sequence of consecutive subproblems which has an optimal substructure. Each subproblem is solved by the hybrid geneticalgorithm and the solutions obtained are extended to be applied to the next subproblem as initial solutions. We examine a wide range of schemes that determine the overall performance of the incremental process and make a number of experiments to verify the effectiveness of each scheme with the synthetic dataset of random graphs. We show that the performance of incremental approach can be significantly improved compared to the previous representative studies by applying appropriate schemes found by the experiments. In addition, we also investigate the effect of different genetic parameters and identify the scalability of our method by conducting experiments using real world dataset with large-sized graphs.
In this paper, we propose an incremental genetic algorithm applied to solve the maximum cut problem. We test the implementation of the algorithm on benchmark graph instances. We propose several methods to build up the...
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ISBN:
(纸本)9781450343237
In this paper, we propose an incremental genetic algorithm applied to solve the maximum cut problem. We test the implementation of the algorithm on benchmark graph instances. We propose several methods to build up the sequence of subproblems, and they are tested through experiments. The performance of a geneticalgorithm makes an improvement when the incremental approach is applied with respect to an appropriate sequence of subproblems.
Measuring similarity between source codes has lots of applications, such as code plagiarism detection, code clone detection, and malware detection. A variety of methods for the measurement have been developed and prog...
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ISBN:
(纸本)9781450342063
Measuring similarity between source codes has lots of applications, such as code plagiarism detection, code clone detection, and malware detection. A variety of methods for the measurement have been developed and program-dependence-graph based methods are known to be well working against disguise techniques. But these methods usually rely on solving NP-hard problems which cause a scalability issue. In this paper, we propose a geneticalgorithm to measure the similarity between two codes by solving an error correcting subgraph isomorphism problem on dependence graphs. We propose a new cost function for this problem, which reflects the characteristic of the source codes. An incremental genetic algorithm is used to solve the problem. The size of the graph to be searched is gradually increasing during the evolutionary process. We developed new operators for the algorithm, and the overall system is tested on some real world data. Experimental results showed that the system successfully works on code plagiarism detection and malware detection. The similarity computed by the system turned out to reflect the similarity between the codes properly.
Finding an isomorphic subgraph is a key problem in many real world applications modeled on graph. In this paper, we propose a new hybrid geneticalgorithm(GA) for subgraph isomorphism problem which uses an incremental...
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ISBN:
(纸本)9781450326629
Finding an isomorphic subgraph is a key problem in many real world applications modeled on graph. In this paper, we propose a new hybrid geneticalgorithm(GA) for subgraph isomorphism problem which uses an incremental approach. We solve the problem with increasing the size of the subproblem step by step. The graph for which we search is gradually expanded from the empty structure to the entire one. We apply a hybrid GA to each subproblem, initialized with the evolved population of previous step. We present design issues for the incremental approach, and the effects of each design decision are analyzed by experiment. The proposed algorithm is tested on widely used dataset. With apposite vertex reordering along with moderate population diversity, incremental approach brought a significant performance improvement. Experimental results showed that our algorithm outperformed representative previous works.
Load frequency control (LFC) is playing an indispensable role to achieve the secure and economic operation of power grids. However, the existing LFC schemes either may rely on a nonlinear grid model under perfect oper...
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Load frequency control (LFC) is playing an indispensable role to achieve the secure and economic operation of power grids. However, the existing LFC schemes either may rely on a nonlinear grid model under perfect operating condition with nominal parameters, or they may adopt a complicated control structure of high order. These LFC schemes may have poor control performance or even loss of stability in real-time implementation due to the grid uncertainties and the change of system operation scenarios. Consequently, a hybrid control method with two control loops considering various practical scenarios is originally proposed in this paper. In the inner loop, variable universe fuzzy logic control is applied to mitigate the impact of load disturbances on control performance. In the outer loop, an incremental genetic algorithm is employed to online optimize the control parameters. The performance of the proposed control method is comprehensively tested on a MATLAB/Simulink-based LFC model and a real-time digital simulator-based real-life 49-bus power system. The extensive results show that the proposed hybrid method exhibits comparatively better control performance than an adaptive fuzzy logic controller and an improved proportion integration controller.
This paper proposes a class decomposition approach to improve the performance of GA-based classifier agents. This approach partitions a classification problem into several class modules in the output domain, and each ...
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This paper proposes a class decomposition approach to improve the performance of GA-based classifier agents. This approach partitions a classification problem into several class modules in the output domain, and each module is responsible for solving a fraction of the original problem. These modules are trained in parallel and independently, and results obtained from them are integrated to form the final solution by resolving conflicts. Benchmark classification data sets are used to evaluate the proposed approaches. The experiment results show that class decomposition can help achieve higher classification rate with training time reduced.
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