A memetic algorithm for the delineation of local labour markets

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.

José M. Casado-Díaz has received financial support from the Spanish Ministry of Transports (Ministerio de Fomento) for the project ”New methods for the analysis of the determinants and effects of commuting flows (MOVICO)” under Grant T 75/2006 (Spanish National Plan of A+D+i). Lucas Martíınez-Bernabeu acknowledges financial support from the Spanish Ministry of Education and Science, the European Social Fund (ESF) and the University of Alicante. Raúl Gómez-Hernández acknowledges financial support from the University of Alicante.