Abstract
In this paper we study a complex real world workforce scheduling problem. We apply constructive search and variable neighbourhood search (VNS) metaheuristics and enhance these methods by using a variable fitness function. The variable fitness function (VFF) uses an evolutionary approach to evolve weights for each of the (multiple) objectives. The variable fitness function can potentially enhance any search based optimisation heuristic where multiple objectives can be defined through evolutionary changes in the search direction. We show that the VFF significantly improves performance of constructive and VNS approaches on training problems, and “learn” problem features which enhance the performance on unseen test problem instances.
This work was funded by EPSRC and @Road Ltd, a Trimble Company under an EPSRC CASE studentship, which was made available through and facilitated by the Smith Institute for Industrial Mathematics and System Engineering.
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Dahal, K., Remde, S., Cowling, P., Colledge, N. (2008). Improving Metaheuristic Performance by Evolving a Variable Fitness Function. In: van Hemert, J., Cotta, C. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2008. Lecture Notes in Computer Science, vol 4972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78604-7_15
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DOI: https://doi.org/10.1007/978-3-540-78604-7_15
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