Transfer Learning Based Evolutionary Algorithm for Bi-level Optimization Problems
Lei Chen, Hai‐Lin Liu · 2021
Evolutionary algorithms for bi-level optimization suffer from the low efficiency in dealing with the multiple lower level optimization tasks required by the population based upper level search. In this paper, we design a transfer learning based covariance matrix adaptation evolution strategy (TL-CMA-ES) for bi-level optimization problems, where the tasks of searching for multiple lower level optimal solutions are conducted by a set of CMA-ES optimizers in a parallel manner. Furthermore, a transfer learning strategy is introduced in the parallel lower level CMA-ES search such that each CMA-ES optimizer can learn and utilize useful features gained by its neighbours. Experimental comparison and analysis are carried out to verify the effectiveness and efficiency of the proposed method.