Abstract
A grid computing environment is a parallel and distributed system that brings together various computing capacities to solve large computational problems. Task scheduling is a critical issue for grid computing; in task scheduling, tasks are mapped onto system processors with the aim of achieving good performance in terms of minimizing the overall execution time. In previous studies, there have been several approaches to solving the task-scheduling problem by genetic algorithms, which is a random search technique that is inspired by natural biological evolution. This study presents a genetic algorithm for solving the problem of task scheduling with two main ideas: a new initialization strategy to generate the first population and new genetic operators based on task---processor assignments to preserve the good characteristics of the found solutions. Our proposed algorithm is implemented and evaluated using a set of well-known applications in our specifically defined system environment. The experimental results show that the proposed algorithm outperforms other popular algorithms in a variety of scenarios with several parameter settings.
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