NeuroLGP-SM: A Surrogate-Assisted Neuroevolution Approach Using Linear Genetic Programming for Efficient Deep Neural Network Optimization

The core message of this paper is to provide insight into the effective integration of surrogate models into neuroevolution, addressing the challenge of high-dimensional data by employing Linear Genetic Programming (NeuroLGP) and Kriging Partial Least Squares (KPLS). The proposed NeuroLGP-SM approach consistently identifies well-performing deep neural networks while reducing the computational requirements compared to a baseline neuroevolutionary method.