An Empirical Investigation on Memes, Self-generation and Nurse Rostering
نویسنده
چکیده
In this paper, an empirical study on self-generating multimeme memetic algorithms is presented. A set of well known benchmark functions is used during the experiments. Moreover, a heuristic template is introduced for solving timetabling problems. The heuristics designed based on this template can utilize a set of constraint-based hill climbers in a cooperative manner. Two such adaptive heuristics are described. Memetic algorithms utilizing each one as if a single hill climber are experimented on a set of random nurse rostering problem instances. Additionally, simple genetic algorithm and two self-generating multimeme memetic algorithms are compared to the proposed memetic algorithms and a previous study.
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Solving a nurse rostering problem considering nurses preferences by graph theory approach
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