In the multíprocessor schedulíng problem a given program is to be scheduled in a given multiprocessor system such that the program 's execution time is minimized. This problem being very hard to solve exactly, many heuristic methods for finding a suboptimal schedule exist. We propose a new combined approach, where a genetic algorithm is improved with the introduction of some knowledge about the scheduling problem represented by the use of a list heuristic in the crossover and mutatíon genetic operations. This knowledge-augmented genetic approach is empirically compared with a "pure" genetic algorithm and with a "pure" list heuristic, both from the literature. Results of the experiments carried out with synthetic instances of the scheduling problem show that our knowledge-augmented algorithm produces much better results in terms of quality of solutions, although being slower in terms of execution time.

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Citation for the original published chapter:Rebreyend, P., Lemarchand, L., Euler, R. (2015) A computational comparison of different algorithms for very large p -median problems.In : Evolutionary Computation in Combinatorial Optimization: 15th European Conference, EvoCOP 2015, Copenhagen, Denmark, April 8-10, 2015 Abstract. In this paper, we propose a new method for solving large scale p-median problem instances based on real data. We compare different approaches in terms of runtime, memory footprint and quality of solutions obtained. In order to test the different methods on real data, we introduce a new benchmark for the p-median problem based on real Swedish data. Because of the size of the problem addressed, up to 1938 candidate nodes, a number of algorithms, both exact and heuristic, are considered. We also propose an improved hybrid version of a genetic algorithm called impGA. Experiments show that impGA behaves as well as other methods for the standard set of medium-size problems taken from Beasley's benchmark, but produces comparatively good results in terms of quality, runtime and memory footprint on our specific benchmark based on real Swedish data.

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