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Applied Computational Intelligence and Soft Computing
Volume 2019, Article ID 6543957, 15 pages
Research Article

Learning Based Genetic Algorithm for Task Graph Scheduling

Department of Computer Science, Faculty of Mathematical Sciences, University of Tabriz, Tabriz, Iran

Correspondence should be addressed to Habib Izadkhah;

Received 20 May 2018; Revised 29 October 2018; Accepted 20 November 2018; Published 3 February 2019

Academic Editor: Yangming Li

Copyright © 2019 Habib Izadkhah. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Nowadays, parallel and distributed based environments are used extensively; hence, for using these environments effectively, scheduling techniques are employed. The scheduling algorithm aims to minimize the makespan (i.e., completion time) of a parallel program. Due to the NP-hardness of the scheduling problem, in the literature, several genetic algorithms have been proposed to solve this problem, which are effective but are not efficient enough. An effective scheduling algorithm attempts to minimize the makespan and an efficient algorithm, in addition to that, tries to reduce the complexity of the optimization process. The majority of the existing scheduling algorithms utilize the effective scheduling algorithm, to search the solution space without considering how to reduce the complexity of the optimization process. This paper presents a learner genetic algorithm (denoted by LAGA) to address static scheduling for processors in homogenous computing systems. For this purpose, we proposed two learning criteria named Steepest Ascent Learning Criterion and Next Ascent Learning Criterion where we use the concepts of penalty and reward for learning. Hence, we can reach an efficient search method for solving scheduling problem, so that the speed of finding a scheduling improves sensibly and is prevented from trapping in local optimal. It also takes into consideration the reuse idle time criterion during the scheduling process to reduce the makespan. The results on some benchmarks demonstrate that the LAGA provides always better scheduling against existing well-known scheduling approaches.