
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.


coremsg

neurolgp-sm-a-surrogate-assisted-neuroevolution-approach-using-linear-genetic-programming-for-efficient-deep-neural-network-optimization


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


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A novel surrogate-assisted neuroevolution approach, named NeuroLGP-SM, efficiently and accurately estimates the fitness of deep neural network architectures without the need for complete evaluations, enabling scalable optimization of large DNN models.


scalable-surrogate-assisted-neuroevolution-for-optimizing-deep-neural-network-architectures


Scalable Surrogate-Assisted Neuroevolution for Optimizing Deep Neural Network Architectures
