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This paper proposes an intelligent trading system using support vector regression optimized by genetic algorithms (SVR-GA) and multilayer perceptron optimized with GA (MLP-GA). Experimental results show that both approaches outperform conventional trading systems without prediction and a recent fuzzy trading system in terms of final equity and maximum drawdown for Hong Kong Hang Seng stock index.
Based on the structure and genetic mechanism of biological DNA, a novel DNA evolution algorithm (NDEA) is presented in this paper. NDEA adopted DNA encoding method, and the novel update strategies of dynamic mutation, structured mutation and population catastrophe is introduced to enhance search capability and to avoid premature convergence. Comparison of NDEA with other algorithms for typical complex...
To choose an appropriate kernel function is one major task for SVM. Different kernel functions will produce different SVMs and may result in different performances. Combined kernel function shows more stable and higher performance than single kernel function, so there is a need to optimize the combined kernel function to enhance the generalization capability of SVM. This paper proposes to optimize...
An effective approach based on the feature of time-frequency atoms for classification of the radar emitter signals is presented. Firstly, we introduce a fast matching pursuit (MP) algorithm, which using improved quantum genetic algorithm (IQGA) to reduce the time-complexity at each step of standard MP, to decompose the signal into a linear expansion of Gaussian chirplet time-frequency atoms. Then,...
In this paper, a novel approach to extract the features of radar emitter signals in the high density, complex and variable signal modulation environment is presented. Based on the over-complete time-frequency atom dictionary, the signals are decomposed into a linear expansion of atoms by the method of matching pursuit (MP). Then, improved quantum genetic algorithm is applied to effectively reduce...
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