LUCIE
Published:
An Evaluation and Selection Method for Stochastic Problems (GECCO 2022)
Selection in genetic algorithms is difficult for stochastic problems due to noise in the fitness space. Common methods to deal with this fitness noise include sampling multiple fitness values, which can be expensive. We propose LUCIE, the Lower Upper Confidence Intervals Elitism method, which selects individuals based on confidence. By focusing evaluation on separating promising individuals from others, we demonstrate that LUCIE can be effectively used as an elitism mechanism in genetic algorithms. We provide a theoretical analysis on the convergence of LUCIE and demonstrate its ability to select fit individuals across multiple types of noise on the OneMax and LeadingOnes problems. We also evaluate LUCIE as a selection method for neuroevolution on control policies with stochastic fitness values.