Behavioral study of the surrogate model-aware evolutionary search framework

The surrogate model-aware evolutionary search (SMAS) framework is an emerging model management method for surrogate model assisted evolutionary algorithms (SAEAs). SAEAs based on SMAS outperform several state-of-the-art SAEAs using other model management methods and show promising results in real-world computationally expensive optimization problems. However, there is little behavioral study of the SMAS framework, and appropriate rules for its search strategy, training data selection and key parameter selection for different types of problems have not been provided yet. In this paper, with a newly proposed training data selection method, the SMAS framework's behaviour with different search strategies and training data selection methods is investigated. The empirical rules in terms of problem characteristics are obtained and the method to construct an SAEA based on the SMAS framework is updated. Experiments using 24 widely used benchmark test problems and the test problems in the CEC 2014 competition of computationally expensive optimization are carried out, which validate the proposed empirical rules.