Fitness Landscape Analysis of TPOT Using Local Optima Network

• Matheus Cândido Teixeira IFMT https://orcid.org/0000-0003-1103-5383
• Gisele Lobo Pappa UFMG https://orcid.org/0000-0002-0349-4494

Resumo

AutoML addresses the challenge of automatically configuring machine learning pipelines for specific data analysis tasks. These pipelines encompass techniques for preprocessing and classifying data. Numerous approaches exist for discovering the optimal pipeline configuration, with most focusing on optimization methods such as Bayesian optimization and evolutionary algorithms. Nevertheless, limited knowledge exists regarding the structure of the search space that these methods operate within. What is certain is that these spaces incorporate categorical, continuous, and conditional hyperparameters, and effectively handling them is not straightforward. To shed light on this matter, the present study conducts an examination of AutoML search spaces generated by the Tree-based Pipeline Optimization Tool (TPOT) algorithm utilizing local optimal networks (LON). The goal is to gain deeper insights into the overall characteristics of the search space, enhancing comprehension of the search strategy employed and the algorithm’s limitations. This investigation aids in understanding the search strategy and constraints of the algorithm, ultimately contributing to the advancement of novel optimization algorithms or the refinement of existing ones within the scientific literature. The findings have implications for enhancing optimization algorithms by illuminating how the search space is explored and the consequent impact on the discovered solutions.