Abstract

The search for suitable parameter values in metaheuristic algorithms is known as the Algorithm Configuration Problem (ACP), which is a challenging problem that directly impacts the performance of target algorithms. Parameter tuners have been proposed to effectively identify suitable values for a set of instances to solve the ACP. However, the search process performed by tuners is a complex and difficult-to-understand task, considering the stochasticity of the tuner, the stochasticity of target algorithms, the quality estimation and definition of decimal precision, the hyperparameters of the tuner and their impact on its performance, among other aspects. In this work, we examine and analyze the search process of the Evolutionary Calibrator (Evoca) tuner using Search Trajectory Networks (STNs). Our goal is to investigate the structure of the configuration networks explored by Evoca and to extract relevant features from …