## 1 Introduction
To deal with this problem, feature reduction, also known as feature selection, is preferred to decrease the computational cost of the ML inference by utilizing a subset of all features. By employing the feature selection methods, it is expected to improve the model's performance metrics, such as accuracy, while reducing the model's complexity and computational cost. Therefore, feature selections are generally preferred to increase the efficiency of ML-based systems. There are several commonly used feature selection algorithms in the literature, such as Recursive Feature Elimination (RFE) [10], Principal Component Analysis (PCA) [11], selectKBest [12], lasso [13], tree-based methods [14], Evolutionary Algorithms (EAs), etc. EAs [15] are a family of optimization techniques that draw inspiration from the principles of natural evolution. These algorithms use a population of possible solutions that change  
Figure 1: Number of phishing attacks, January 2019 to April 2023 [3]. over time through a process of selection, mutation, and recombination to find an optimal or near-optimal solution to a problem. They are preferred in many optimization problems, just as they were in feature selection problems, because they are robust, flexible, can handle non-linear and non-differentiable problems, can work in parallel, or can optimize for more than one goal. Genetic Algorithms (GAs) are evolutionary optimization techniques that try to find the best solution to a problem by imitating how natural selection works. GAs have been used to solve a wide range of optimization problems, such as feature selection, with great success. In this concept, our main challenge fits in a multi-objective optimization problem by increasing the detection accuracy and recall value of the trained system, while decreasing the processing time by minimizing the number of features to use. The use of GAs for feature selection involves encoding the features into chromosomes and then applying genetic operators such as selection, crossover, and mutation to evolve a population of candidate solutions. Each solution is judged on how well it works with a given objective function, and the process is repeated until a good solution is found. This paper aims to provide a comprehensive understanding of GA-based feature selection and its potential to improve the performance of ML models by selecting the optimal subset of features and reducing the dimensions of the data. By presenting the latest research in this area, we hope to inspire future work and encourage researchers to explore this promising approach in their applications. In order to conduct a thorough and efficient systematic study, it is imperative to establish well-defined research questions. The research questions formulated for the present investigation are outlined below:  
RQ 1: How can an evolutionary approach(genetic algorithm) be used during the feature selection of machine learning models in the URL-based phishing detection system? RQ 2: What are the effects of a genetic-algorithm-based feature selection strategy on machine learning models? RQ 3: What are the strengths and weaknesses of the genetic-algorithm-based feature selection strategy compared to greedy-based feature selection algorithms? The rest of the paper is organized as follows: In Section 2, related works such as phishing detection, ML, and GA-based systems are detailed by showing the related works in the literature. Section 3 explains the design details and algorithms of the proposed solution for a genetic-algorithm-based feature selection algorithm in a phishing detection system. The empirical results of the proposed system are analyzed by comparison with other feature selection models and are also illustrated in Section 5 after explaining the experimental setup in Section 4. Finally, after discussing the crucial aspects of the genetic-algorithm-based feature selection method in Section 6, the paper is concluded in Section 7 including future work that can be conducted in related areas.