A Population Prescreening Strategy for Kriging-Assisted Evolutionary Computation

Prescreening strategies have been widely used in surrogate-assisted evolutionary algorithms for screening out poor solutions. Existing prescreening strategies are designed for individual-level selection, i.e. they are used to select individuals from a set of population members. In this work, we propose a population prescreening strategy based on the multi-point expected improvement criterion for Kriging-assisted evolutionary algorithms. In each generation of the proposed algorithm, the evolutionary operators are used repeatedly to generate a set of candidate populations. Then, these candidate populations are prescreened by the multi-point expected improvement criterion and the population with highest multi-point expected improvement value is selected for the next generation. Following this, infill criteria are used to select promising solutions from the selected population for expensive evaluation. Numerical experiments on eighteen test problems show that the proposed population prescreening strategy can improve the optimization efficiency of the Kriging-assisted evolutionary algorithms significantly without introducing too much additional computational cost.