## 1 Introduction  
In the last two decades, various approximate algorithms were introduced, focussing on their basic guidelines to quickly find feasible and suitable solutions to complex problems. These algorithms are commonly referred to as metaheuristic algorithms [1, 2]. Among these algorithms, evolutionary algorithms (EAs), which include Genetic Algorithm (GA), Differential Evolution (DE), Evolutionary Strategy (ES), and Genetic Programming (GP), have been growing very quickly in terms of research developments and application areas [3]. GA has attracted the most researchers due to its various advantages, such as its ability to solve combinatorial hard problems with multiple solutions using a global search and its suitability for broader classes of problems [4, 5]. These classes of algorithms produce a large amount of data during the search process. Unfortunately, these algorithms do not analyze the data generated during the search process. However, artificial intelligence, such as machine learning (ML), can help to analyze the generated data and provide helpful information from the search results, making EAs more intelligent and powerful. Luckily, the Artificial Intelligence (AI) research is flourishing at rocket speed in each sector of our daily life [6]. ML is a promising and significant research field in AI, which has been developing very rapidly and is used in various problems as a solo or an integrated part of a wider solution methodology [7]. Therefore, it is reasonable to assume that the enhancement of EAs (in terms of both solution quality and convergence speed) can be accomplished by using ML during the search process [3; 8]. In the literature, different ML techniques have been used in various EAs. ANN is an ML technique that is widely used in the engineering, automotive, medicine, economics, aerospace, and energy sectors, and in many other fields [6; 9; 10]. ANN is adopted to limit the GA's search space in some applications, such as silicon clustering and allocation problems [11; 12]. [PERSON] et al. [13] used GA and ANN to maximize biosurfactant production. The statistical experimental strategy was used to obtain the results, which was further used to link the ANN with GA. It was reported that the proposed hybrid system could boost production by nearly 70%. [PERSON] et al. [14] used a back mutation-based genetic neural network instead of backpropagation ANN. The proposed approach gave the network dynamics a good structure. This system helps to consider a wider range of populations with more flexibility and less restriction. [PERSON] et al. [15] used ANN-based GA to design materials. Their study's main aim was to give GA the ability to learn and speed up the evolutionary process. [PERSON] [16] also used GA and augmented NN to solve the online advertisement scheduling issue. Moreover, [PERSON] et al. [17] considered the combination of ANN and GA when evaluating the stock price trend in Thailand. [PERSON] et al. [18] used the ART-1 network with tabu search optimization technique to solve the cell formation problems. The ART ANN was only used to find an initial solution. The use of machine-learning techniques as a mechanism to deal with initial solution generation has also been widely reported (see, for instance, [19; 20; 21; 22]). [PERSON] and [PERSON] [23] proposed the use of an ARTMAP neural network as a fitness evaluator of GA. The authors showed that ART could be used to generate clusters depending on the vigilance value. Furthermore, [PERSON] and [PERSON] [24] also proposed their ART as a fitness evaluator of GA for musical composition. The use of ML as a mechanism to evaluate the fitness of a solution was also reported in several other publications. A neural-network-embedded GA was proposed to solve a non-linear time-cost tradeoff problem for project scheduling in [25]. The ANN was used for the rapid estimation of the time-cost relationship. Other research articles that apply ML for fitness evaluation include [26; 27; 28; 29; 30].