Evolution Control Ensemble Models for Surrogate-Assisted Evolutionary Algorithms

Finding the trade-off between exploitation and exploration in a Surrogate-Assisted Evolutionary Algorithm is challenging as the focus on the landscape being optimized moves during the search. The balancing is mainly guided by Evolution Controls, that decide to simulate, predict or discard newly generated candidate solutions. Combining Evolution Controls in ensembles allows to regulate the degree of exploitation and exploration during the search. In this study, we propose ensemble strategies between multiple Evolution Controls in order to adapt the trade-off for each region scrutinized during the search. Experiments led on benchmark problems and on a real-world application of SARS-CoV-2 Transmission Control reveal that favoring exploration at the beginning of the search and favoring exploitation at the end of the search is beneficial in many cases.