## 3 Methodology  
We used the modified NAS that is originally proposed in [6] in this experiment. It uses genetic algorithms, where several genetic operators make different offsprings from their parents. The proposedmethod adopts a special genetic operation, called the chromosome non-disjunction, in order to allow the destructive searching not possible in conventional auto-design artificial neural networks (ANN) architectures [6]. The chromosome non-disjunction operation provides the variability for designing an ANN architecture. Consequently, our approach does not need to start from the minimum architecture. Instead, the designer should define the initial ANN architecture.  
Figure 1 shows the system configuration of a modified NAS. It consists of a genetic algorithm (GA) generator and a group of neural networks (NN) generators. The genetic operator has three operations: (i) a cross-over operation that blends information of parents to make various but parent-like offspring; (ii) a mutation operation that changes a random gene to make it different from parents; and (iii) a non-disjunction operation [6] that makes two offspring where one has less information and the other one has more information.  
The GA operator has a role in controlling the population. It makes new generations of individuals which present ANN architectures. The genetic operator consists of selection, cross-over, mutation, and non-disjunction. The selection operator selects the appropriate individuals which survive from the application of the fitness function as defined in Equation (1). The cross-over operator mixes the chromosomes between the two parents. The mutation operator mutates a part of the information of the parents. The non-disjunction can add or discard the layers or the connections.  
The chromosomes are organized with two types: neuron type and connection type. The neuron type of chromosome contains the information of the neuron or layer. It presents the unique number of layers, the numbers of inputs, activation function, stride, etc. The connection type of chromosome contains the information of the connection between two layers with the unique number of them. We refer readers to [PERSON] et al [6] for further details on the algorithm of generating NN using chromosomes.  
The NN generator designs neural architectures from chromosomes in individuals and evaluates them. The deep neural architecture generator interprets chromosomes with a node checker and link  
Figure 1: Overall methodology.  
checker. Every individual has several chromosomes, which means layers and links. However, not all individuals are runnable and learnable. Some have broken connections, and some have unmatched input/output. The model checker and link checker classify these inappropriate neural architectures. The NN architecture evaluator implements training and testing with deep learning. Users must prepare preprocessed training data and testing data. Then, fitness values, which are calculated by NN generators, are sent to the GA operator.  
The accuracy of the experiment is calculated by fitness function. Fitness function (Equation (1)) is defined as follows:  
\[fitness=(1-\text{loss})-R*\frac{num_{layer}}{num_{avg}}*(2*(1-loss)-1) \tag{1}\]  
where \(R\) means the coefficient of dependency rate of the number of layers between 0 to 1. \(Num_{layer}\) means the number of layers and \(num_{avg}\) means the average of the number of layers.