Title: An enhance DE algorithm for analysis in dataset

Authors: Dharmpal Singh; J. Paul Choudhury; Mallika De

Addresses: JIS College of Eng?neer?ng, Block 'A' Phase I??, Kalyan?, Nad?a, West Bengal, 741235, India ' Department of Information Technology, Kalyani Government Engineering College, Kalyani, Nadia, West Bengal, 741235, India ' Department of Computer Science and Engineering, Dr. Sudhir Sur Degree Engineering College, 540 Dum Dum Road, Kolkata, West Bengal, 700074, India

Abstract: Differential evolution (DE) is a simple, powerful optimisation algorithm widely used to solve constrained optimisation problems, multi objective global optimisations and complex real-world applications. However, the choices of the best mutation, search strategies, long training time and lower classification accuracy are difficult for authors to know the appropriate encoding schemes and evolutionary operators. Otherwise, it may be lead to demanding computational costs of the time consuming trial and error parameter and operator tuning process. Moreover, mutation and crossover plays an important role in the DE optimisation and several functions are available for it may leads a different result for the same dataset. Therefore, an enhance DE has been proposed to improve searching ability, crossover and mutation strategy to efficiently guide the evolution of the population towards the global optimum in less time.

Keywords: data mining; association rule; data preprocessing; factor analysis; fuzzy logic; neural network; PSO; particle swarm optimisation; artificial bee colony; DEA; differential evolution algorithm.

DOI: 10.1504/IJDS.2018.094503

International Journal of Data Science, 2018 Vol.3 No.3, pp.203 - 235

Received: 20 Oct 2015
Accepted: 02 Oct 2016
Published online: 04 Sep 2018 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article