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A Hybrid Model of FLANN and Firefly Algorithm for Classification
Abstract
Since last decade, biologically inspired optimization techniques have been a keen interest among the researchers of optimization community. Some of the well developed and advanced popular algorithms such as GA, PSO etc. are found to be performing well for solving large scale problems. In this chapter, a recently developed nature inspired firefly algorithm has been proposed by the combination of an efficient higher order functional link neural network for the classification of the real world data. The main advantage of firefly algorithm is to obtain the solutions for global optima, where some of the earlier developed swarm intelligence algorithms fail to do so. For learning the neural network, efficient gradient descent learning is used to optimize the weights. The proposed method is able to classify the non-linear data more efficiently with less error rate. Under null-hypothesis, the proposed method has been tested with various statistical methods to prove its statistical significance.
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