Local labour markets delineation: an approach based on evolutionary algorithms and classification methods

In this paper a methodology for the delineation of local labour markets (LLMs) using evolutionary algorithms is proposed. This procedure, based on that in Flórez-Revuelta et al. [13 F. Flórez-Revuelta, J.M. Casado-Díaz, and L. Martínez-Bernabeu, An evolutionary approach to the delineation of functional areas base on travel-to-work flows, Int. J. Autom. Comput. 5(1) (2008), pp. 10–21. doi: 10.1007/s11633-008-0010-6[Crossref] , [Google Scholar],14 F. Flórez-Revuelta, J.M. Casado-Díaz, L. Martínez-Bernabeu, and R. Gómez-Hernández, A memetic algorithm for the delineation of local labour markets, in Parallel Problem Solving from Nature X, Vol. 5199, Lecture Notes in Computer Science, G. Rudolph, T.H. Jansen, S.M. Lucas, C. Poloni, and N. Beume, eds., Springer, Berlin, 2008, pp. 1011–1020. [Google Scholar]], introduces three modifications. First, initial groups of municipalities with a minimum size requirement are built using the travel time between them. Second, a not fully random initiation algorithm is proposed. And third, as a final stage of the procedure, a contiguity step is implemented. These modifications significantly decrease the computational times of the algorithm (up to a 99%) without any deterioration of the quality of the solutions. The optimization algorithm may give a set of potential solutions with very similar values with respect to the objective function what would lead to different partitions, both in terms of number of markets and their composition. In order to capture their common aspects an algorithm based on a cluster partitioning of k-means type is presented. This stage of the procedure also provides a ranking of LLMs foci useful for planners and administrations in decision-making processes on issues related to labour activities. Finally, to evaluate the performance of the algorithm a toy example with artificial data is analysed. The full methodology is illustrated through a real commuting data set of the region of Aragón (Spain).