Modified Neural Architecture Search (NAS) Using the Chromosome Non-Disjunction

Abstract

This paper proposes a deep neural network structuring methodology through a genetic algorithm (GA) using chromosome non-disjunction. The proposed model includes methods for generating and tuning the neural network architecture without the aid of human experts. Since the original neural architecture search (henceforth, NAS) was announced, NAS techniques, such as NASBot, NASGBO and CoDeepNEAT, have been widely adopted in order to improve cost- and/or time-effectiveness for human experts. In these models, evolutionary algorithms (EAs) are employed to effectively enhance the accuracy of the neural network architecture. In particular, CoDeepNEAT uses a constructive GA starting from minimal architecture. This will only work quickly if the solution architecture is small. On the other hand, the proposed methodology utilizes chromosome non-disjunction as a new genetic operation. Our approach differs from previous methodologies in that it includes a destructive approach as well as a constructive approach, and is similar to pruning methodologies, which realizes tuning of the previous neural network architecture. A case study applied to the sentence word ordering problem and AlexNet for CIFAR-10 illustrates the applicability of the proposed methodology. We show from the simulation studies that the accuracy of the model was improved by 0.7% compared to the conventional model without human expert.