using performance fronts for parameter setting of stochastic metaheuristics
In this work, we explore the idea that parameter setting of stochastic metaheuristics should be considered as a multi-objective problem. The so-called "performance fronts" presented in this work are a collection of non-dominated parameters sets, satisfying both a speed and a precision objective. Experiments are conducted using a multi-objective evolutionary algorithm, in order to: (i) set a parameter of several continuous metaheuristics, and (ii) set parameters of an hybrid algorithm for temporal planning. Our results suggest that the performance fronts are well suited for setting the parameters of stochastic metaheuristics. The relative position, in the objective space, of several parameter fronts also permits to compare metaheuristics on a given problem. Moreover, this approach give insights on the algorithm behaviour.
parameter setting metaheuristics evolutionary computation multi-objective optimization performance assessment