# Modified Neural Architecture Search (NAS) Using the Chromosome Non-Disjunction
## 1 Introduction  
Deep learning techniques have been successfully applied in multiple fields, including engineering, in recent years due to their state-of-the-art problem-solving performance [1, 2]. Examples include image recognition [3, 4], natural language processing (NLP) [5, 6], game artificial intelligence (AI) [7], self-driving systems [8, 9], and agriculture [10]. Furthermore, open-source toolkits for deep learning have become more varied and easier to use [11]. These programming libraries are easily accessible to non-experts. The majority of recent research in deep learning has been focused on finding better architectures [12]. In other words, designing neural network architecture plays a vital role in deep learning [13]. However, since most of the available architectures have been developed manually, the entire process is time-consuming and susceptible to errors [14]. Furthermore, the larger scale of current deep neural networks (DNNs) results in the more complex structure of the DNNs [15]. Thus, the design and optimization of architecture have become challenging for designers. To solve this problem, many researchers have tried to automate DNN design [15, 16, 17]. The automated designing methods for effective DNN architecture are state-of-the-art technologies in studies on deep learning architecture [18], which can reduce the costs and time for the model deployment [18]. Neural architecture search (NAS) is a technique used to automate the design process of DNNs. NAS has been known as a subcategory of automated machine learning (AutoML), and it contains significant overlap with hyper-parameter optimization [14]. NAS encompasses a broad set of techniques that design DNN architecture automatically for a given task [18; 19]. NAS has three challenges: designing network architecture, putting components into a network topology, and hyper-parameter tuning [15]. Optimization and study of each factor are necessary for generating of a new task. There are several types of approaches to NAS, including grid search [20], the Bayesian-based Gaussian process (BGP) [17; 21; 22], tree-structured Parzen estimators [23], and the evolutionary algorithm (EA). Grid search tests all combinations of the parameters to determine the best one. For this reason, it is difficult for grid search to evaluate all combinations within an acceptable amount of time [24]. The BGP and tree-structured Parzen estimators are suitable for hyper-parameter optimization (HPO). Bayesian optimization (BO) is a representative case of the HPO methodology [16; 17]. An EA is a type of population-based heuristic computational paradigm [25; 26]. It has been widely used in the area of optimization problems [27; 28; 29]. In evolutionary deep learning (EDL) methodologies, each individual is transformed into a DNN with the corresponding architecture through the mapping from genotype and phenotype [30]. Several EAs, such as particle swarm optimization (PSO) [31; 32; 33; 34], the gravitational search algorithm (GSA) [35; 36; 37], grey wolf optimizer (GWO) [38; 39; 40], firefly algorithm (FA) [41], and genetic algorithm (GA) [12; 28; 29; 42] have been implemented to design neural architectures. A GA is a main stream of the EAs, which does not require rich domain knowledge [25; 43], and is applied to various fields due to their characteristics of being gradient-free and insensitive to local minima [44]. A GA contains several genetic operators, such as selection, mutations, and cross-overs. These operators realize the GA suitable for the optimization of DNN architecture design [24]. However, most of these EDLs offer methods that stack layers without dynamic links. This paper represents a modified NAS as a DNN structuring methodology through the GA using chromosome non-disjunction [12]. This method does not include hyper-parameter optimization. Instead, it focuses on an automated design process of the full network topology containing dynamic links via the EA.