Adaptive Chaotic Cultural Algorithm for Hyperparam

Authors: Jian Cheng Jiansheng Qian Yinan Guo

Publish Date: 2009/9/16

Volume: , Issue: , Pages: 286-293

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Abstract

The hyperparameters selection has a great affection on accuracy of support vector regression SVR In order to determine the hyperparameters of SVR an adaptive chaotic cultural algorithm ACCA is employed for the optimal hyperparameters including kernel parameters σ of Gaussian kernel function regular constant γ and ε in the εinsensitive loss function Based on this a learning algorithm with twostage is constructed to realize the objective Firstly the initialization search spaces of hyperparameters are determined according to their influence on the performance of support vector regression Secondly optimal hyperparameters are selected using ACCA ACCA adopt dual structure in cultural algorithm and adaptive chaotic mutation in evolution induction functions and uses implicit knowledge extracted from evolution process to control mutation scale which inducts individuals escaping from local best solutions Taken the forecasting of gas concentration as example experiment results indicate optimal hyperparameters can be obtained through above strategy

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