Integrating Random Ordering into Multi-heuristic List Scheduling Genetic Algorithm

This paper presents an extension of a Multi-heuristic List Scheduling Genetic Algorithm. The problem is to find a schedule that will minimize the execution time of a program in multi-processor platform. Because this problem is known to be NP-complete, many heuristics have been developed. List Scheduling is one of the classical heuristic solutions. The idea of Multi-heuristic List Scheduling Genetic Algorithm is to find an “optimal” combination of List Scheduling heuristics, which outperforms only one heuristic. However, a very important drawback of this algorithm is that it does not take all the search space into consideration. We improved it by introducing random ordering that allows it to cover the entire search space while at the same time focusing on the heuristics region. An experimental comparison is then made to against both Multi-heuristic List Scheduling Genetic Algorithm and Combined Genetic-List Algorithm.

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