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Title: Stopping rules for a parallel genetic algorithm

Authors: Ioannis G. Tsoulos; Alexandros Tzallas; Markos Tsipouras; Vasileios Christou; Dimitrios Tsalikakis

Addresses: Department of Informatics and Telecommunications, University of Ioannina, Greece ' Department of Informatics and Telecommunications, University of Ioannina, Greece ' Department of Informatics and Telecommunications, University of Ioannina, Greece ' School of Computer Science, The University of Manchester, Oxford Rd, M13 9PL, Manchester, UK ' Department of Engineering Informatics and Telecommunications, University of Western Macedonia, Greece

Abstract: A novel method for the implementation of parallel genetic algorithms is introduced to locate the global minimum of a multidimensional function inside a rectangular hyperbox. The algorithm relies on a client - server model and incorporates an enhanced stopping rule. A number of experiments were conducted in order to measure the effects in termination by using the termination rule either on server machine or on clients. The method is tested on a series of well - known test functions as well as neural network training and the results was compared against another parallel genetic algorithm method. The results from the experiments are reported in terms of test error and amount of generations.

Keywords: genetic algorithms; parallel algorithms; stopping rules; optimisation.

DOI: 10.1504/IJCISTUDIES.2020.106498

International Journal of Computational Intelligence Studies, 2020 Vol.9 No.1/2, pp.146 - 160

Received: 09 Aug 2018
Accepted: 14 Nov 2018

Published online: 06 Apr 2020 *

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