Evolving One-Dimensional Deep Convolutional Neural Network: A Swarm Based Approach

This paper proposes a new approach for evolving One-Dimensional Convolutional Neural Network (1D-CNN). A Particle Swarm Optimization (PSO) algorithm was incorporated to evolve the network layers and parameters. The proposed approach was evaluated over a real-world rainfall prediction application. It was compared to a trial and error approach and to the first version of the official rainfall prediction system used by the Bureau of Meteorology in Australia. Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Pearson correlation (r) statistical measurements were calculated to measure the performance of each approach. The performance of the proposed approach was promising compared to existing state-of-the art methods. This proposed framework can be easily extended and deployed to other applications including machine vision.