Gradient Based Evolution to Optimize the Structure of Convolutional Neural Networks

Abstract

Due to decreasing hardware prices, machine learning is becoming increasingly interesting for industrial applications such as automatic visual inspection (AVI). This paper presents a metaheuristic approach to the automatic generation of a well suited convolutional neural network (CNN) based on differential evolution. This makes it possible to find a suitable architecture of a CNN for a given task with little prior knowledge. Another aim is to reduce the resources needed in the inference as much as possible. Therefore, we choose a function that considers both the accuracy and the resources used to measure the fitness of a CNN. For typical industrial datasets, we obtain CNNs with an accuracy of more than 98 % on average within relatively short processing time.