BenchENAS : A Benchmarking Platform for Evolutionary Neural Architecture Search

Neural architecture search (NAS), which automatically designs the architectures of deep neural networks, has achieved breakthrough success over many applications in the past few years. Among differ…

Evolving Deep Convolutional Variational Autoencoders for Image Classification

Variational autoencoders (VAEs) have demonstrated their superiority in unsupervised learning for image processing in recent years. The performance of the VAEs highly depends on their architectures,…

A Novel Training Protocol for Performance Predictors of Evolutionary Neural A…

Evolutionary neural architecture search (ENAS) can automatically design the architectures of deep neural networks (DNNs) using evolutionary computation algorithms. However, most ENAS algorithms req…

A New Two-Stage Evolutionary Algorithm for Many-Objective Optimization

Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Fore…

Convolutional neural networks (CNNs) have shown remarkable performance in various real-world applications. Unfortunately, the promising performance of CNNs can be achieved only when their architect…

Evolving Deep Convolutional Neural Networks for Image Classification

Evolutionary paradigms have been successfully applied to neural network designs for two decades. Unfortunately, these methods cannot scale well to the modern deep neural networks due to the complic…