Multi-objective optimization with controlled model assisted evolution strategies

Evolutionary algorithms perform robust search in complex and high dimensional search spaces, but require a large number of fitness evaluations to approximate optimal solutions. These characteristics limit their potential for hardware in the loop optimization and problems that require extensive simulations and calculations. Evolutionary algorithms do not maintain their knowledge about the fitness function as they only store solutions of the current generation. In contrast, model assisted evolutionary algorithms utilize the information contained in previously evaluated solutions in terms of a data based model. The convergence of the evolutionary algorithm is improved as some selection decisions rely on the model rather than to invoke expensive evaluations of the true fitness function. The novelty of our scheme stems from the preselection of solutions based on an instance based fitness model, in which the selection pressure is adjusted to the quality of model. This so-called lambda-control adapts the number of true fitness evaluations to the monitored model quality. Our method extends the previous approaches for model assisted scalar optimization to multi-objective problems by a proper redefinition of model quality and preselection pressure control. The analysis on multi-objective benchmark optimization problems not only confirms the superior convergence of the model assisted evolution strategy in comparison with a multi-objective evolution strategy but also the positive effect of regulated preselection in contrast to merely static preselection.