Metaheuristics have been successfully applied to solve complex real-world problems in many application domains. Their performance strongly depends on the values of their parameters. Many tuning algorithms have already been proposed to find a set of suitable values. However, the amount of computational time required to obtain these values is usually high. Our goal is to propose a collaborative strategy to help to reduce the configuration effort during the tuning process. Here, we introduce a novel initialization strategy that learns from poor quality configurations in a pre-processing phase. We evaluate our collaboration using the well-known Evolutionary Calibrator (Evoca). Moreover, we tune two different algorithms: the Ant Knapsack algorithm, using hard instances of the Multidimensional Knapsack Problem, and a Genetic Algorithm for solving landscapes that follow the NK model (N components and degree K). Evoca obtains promising results using our novel strategy, consuming less computational resources.