Adaptive evolutionary algorithms 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. The experimentation process showed that the rate of success of each operator in generating good individuals is different and evolves with time. We therefore propose different adaptive alternatives that modify the probabilities of application of each operator throughout the evolutionary process, and compare the results of such adaptive approaches with previous results and a greedy method.