Injecting social diversity in multi-objective genetic programming: The case of model well-formedness rule learning

Abstract

Software modelling activities typically involve a tedious and time-consuming effort by specially trained personnel. This lack of automation hampers the adoption of the Model Driven Engineering (MDE) paradigm. Nevertheless, in the recent years, much research work has been dedicated to learn MDE artifacts instead of writing them manually. In this context, mono- and multi-objective Genetic Programming (GP) has proven being an efficient and reliable method to derive automation knowledge by using, as training data, a set of examples representing the expected behavior of an artifact. Generally, the conformance to the training example set is the main objective to lead the search for a solution. Yet, single fitness peak, or local optima deadlock, one of the major drawbacks of GP, remains when adapted to MDE and hinders the results of the learning. We aim at showing in this paper that an improvement in populations’ social diversity carried out during the evolutionary computation will lead to more efficient search, faster convergence, and more generalizable results. We ascertain improvements are due to our changes on the search strategy with an empirical evaluation featuring the case of learning well-formedness rules in MDE with a multi-objective genetic algorithm. The obtained results are striking, and show that semantic diversity allows a rapid convergence toward the near-optimal solutions. Moreover, when the semantic diversity is used as for crowding distance, this convergence is uniform through a hundred of runs.