Research Area:  Metaheuristic Computing

Abstract:

Convolutional neural networks (CNN) are highly effective for image classification and computer vision activities. The accuracy of CNN architecture depends on the design and selection of optimal parameters. The number of parameters increases exponentially with every connected layer in deep CNN architecture. Therefore, the manual selection of efficient parameters entirely remains ad-hoc. To solve that problem, we must carefully examine the relationship between the depth of architecture, input parameters, and the model’s accuracy. The evolutionary algorithms are prominent in solving the challenges in architecture design and parameter selection. However, the adoption of evolutionary algorithms itself is a challenging task as the computation cost increases with its evolution. The performance of evolutionary algorithms depends on the type of encoding technique used to represent a CNN architecture. In this article, we presented a comprehensive study of the recent approaches involved in the design and training of CNN architecture. The advantages and disadvantages of selecting a CNN architecture using evolutionary algorithms are discussed. The manual architecture is compared against automated CNN architecture based on the accuracy and range of parameters in the existing benchmark datasets. Furthermore, we have discussed the ongoing issues and challenges involved in evolutionary algorithms-based CNN architecture design.

Keywords:  
Evolutionary algorithms
Genetic algorithm
Particle swarm optimization
Convolutional neural networks
Deep learning
Metaheuristic Optimization

Author(s) Name:  Vidyanand Mishra & Lalit Kane

Publisher name:  Springer

DOI:  10.1007/s10462-022-10303-4

Volume Information:  

Paper Link:   https://link.springer.com/article/10.1007/s10462-022-10303-4