Crow Search Algorithm (CSA) is a bio-inspired meta-heuristic optimizer which simulates the intelligent behavior of crows. CSA was proposed by Alireza Askarzadeh in 2016. It is a population-based algorithm that works on the following four principles: Crows live in the form of groups or flocks, Crows memorize the position of their food hiding places, Crows follow each other to do thievery, Crows protect their caches from being pilfered by a probability. CSA has been developed based on intelligent behaviors and used them as an optimization process.
Published in Chapter:
Crow-ENN: An Optimized Elman Neural Network with Crow Search Algorithm for Leukemia DNA Sequence Classification
Rehan Ullah (The University of Agriculture, Peshawar, Pakistan), Abdullah Khan (The University of Agriculture, Peshawar, Pakistan), Syed Bakhtawar Shah Abid (The University of Agriculture, Peshawar, Pakistan), Siyab Khan (The University of Agriculture, Peshawar, Pakistan), Said Khalid Shah (Department of Computer Science, University of Science and Technology, Bannu, Pakistan), and Maria Ali (The University of Agriculture, Peshawar, Pakistan)
Copyright: © 2020
|Pages: 41
DOI: 10.4018/978-1-7998-2521-0.ch009
Abstract
DNA sequence classification is one of the main research activities in bioinformatics on which, many researchers have worked and are working on it. In bioinformatics, machine learning can be applied for the analysis of genomic sequences like the classification of DNA sequences, comparison of DNA sequences. This article proposes a new hybrid meta-heuristic model called Crow-ENN for leukemia DNA sequences classification. The proposed algorithm is the combination of the Crow Search Algorithm (CSA) and the Elman Neural Network (ENN). DNA sequences of Leukemia are used to train and test the proposed hybrid model. Five other comparable models i.e. Crow-ANN, Crow-BPNN, ANN, BPNN and ENN are also trained and tested on these DNA sequences. The performance of models is evaluated in terms of accuracy and MSE. The overall simulation results show that the proposed model has outperformed all the other five comparable models by attaining the highest accuracy of over 99%. This model may also be used for other classification problems in different fields because it can achieve promising results.