Surrogate-Assisted Evolution of Convolutional Neural Networks by Collaboratively Optimizing the Basic Blocks and Topologies

Convolutional neural networks (CNNs) are prominent in many fields owing to their outstanding feature extraction abilities. Many excellent CNNs have been carefully designed by algorithm researchers; however, the design process is limited by the inherent knowledge of the researchers. Inspired by the existing successful block-based neural architecture search, we develop a collaboratively automatically evolutionary CNNs algorithm (CAE-CNN), which employs a surrogate-assisted genetic algorithm to search for a satisfactory CNN architecture by collaboratively optimizing the basic blocks in ResNet and DenseNet and their topologies. The encoding space of CAE-CNN consists of three basic units (pooling, ResNet, and DenseNet units) and a connection topology with a variable size. To effectively evolve the population, we design a double-module crossover operation and a multi-type mutation operation to collaboratively evolve the units and the topology. To address the problem of a rapidly increasing search space caused by the topological search, we use a random forest as the surrogate model to estimate the fitness of an individual to accelerate the search. CAE-CNN is a completely automatic algorithm in which a satisfactory CNN architecture can be obtained without any manual intervention. Experimental results show that CAE-CNN could archive competitive performance in terms of classification accuracy on four image classification datasets, and it consumes fewer computing resources than many algorithms.