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