A Memetic Algorithm for the Delineation of Local Labour Markets

  • Authors:

  • Francisco Flórez-Revuelta;José Manuel Casado-Díaz;Lucas Martínez-Bernabeu;Raúl Gómez-Hernández

  • Affiliations:

  • Research Unit on Industrial Computing and Computer Networks, University of Alicante, Alicante, Spain E-03080;Institute of International Economics, University of Alicante, Alicante, Spain E-03080;Research Unit on Industrial Computing and Computer Networks, University of Alicante, Alicante, Spain E-03080;Research Unit on Industrial Computing and Computer Networks, University of Alicante, Alicante, Spain E-03080

  • Venue:
  • Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
  • Year:
  • 2008

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Abstract

Given a territory composed of basic geographical units, the delineation of local labour market areas (LLMAs) can be seen as a problem in which those units are grouped subject to multiple constraints. In previous research, standard genetic algorithms were not able to find valid solutions, and a specific evolutionary algorithm was developed. The inclusion of multiple ad hoc operators allowed the algorithm to find better solutions than those of a widely-used greedy method. However, the percentage of invalid solutions was still very high. In this paper we improve that evolutionary algorithm through the inclusion of (i) a reparation process, that allows every invalid individual to fulfil the constraints and contribute to the evolution, and (ii) a hillclimbing optimisation procedure for each generated individual by means of an appropriate reassignment of some of its constituent units. We compare the results of both techniques against the previous results and a greedy method.