## 1 Introduction  
Day by day the number of mobile users exceptionally increases and approximately surpasses the 2.5 billion users worldwide [1]. Android is an operating system manufactured using a Linux-based kernel for cellular phones. Recently, it has been considerably popular for smartphones and the Internet of things devices. Even that, it constitutes a favorable environment for malicious applications to disseminate. Malware is a malicious software accesses and hijacks computer or mobile systems, which eventually could damage, steal, encrypt, or disable the system files or data. Malware software comes in different forms such as spyware, ransomware, trojans, worms, viruses, or others. In [2], it had been reported that a single family of mobile ransomware had a negative effect on one million Android users in one month. Ransomware is a kind of malware software that hinders the user with the infected mobile from accessing their data due to either data encryption or device locking, while the attacker waits for a ransom to decrypt the data or unlock the cellular device. Even that its source code is available online but safeguarding against it is hard [3]. Indeed, it is a requirement to design an effective ransomware detection approach. In the literature, the research community developed different solutions to combat ransomware, such as the use of honeypots, or the use of statistical methods. These approaches are facing challenges such as the high false positive rates, or a limited capacity to detect complex, persistently-evolved ransomware attacks [4; 5] ideally before the files are encrypted or the system is damaged. Artificial intelligence, machine learning, and deep learning tools have been elucidating very efficient abilities in capturing hidden patterns of information and inferring useful knowledge that would be capable of unraveling intricate attacks. Machine learning approaches that are devoted to malware detection rely on utilizing a learning algorithm that learns discriminating characteristics of a malicious application and successfully classifies it into a malware application or a benign application. However, the constantly increasing number of mobile applications as well as the rapid growth of malware families, in particular, the ransomware; have led to the requirement of developing a more resilient and robust detection algorithm. The performance of machine learning algorithms is significantly affected by various factors; such as the settings of the hyperparameters or the presence of irrelevant, redundant features. Thereby, developing an efficient malware detection algorithm requires handling the variable, high-dimensional attributes of malwares, while setting the hyperparameters properly. A fundamental preprocessing step during modeling a machine learning algorithm is the feature selection. Feature selection is concerned with removing the irrelevant redundant features which deteriorate the performance of a learning algorithm. Three major approaches are identified for performing feature selection; filter-based, wrapper-based, and embedded-based approaches. Filter methods assess and rank the features independently from the learning algorithm like the correlation filter. Whereas, the wrapper methods depend on a learning algorithm to evaluate potential subsets of features and score the best one that has the highest prediction power. Embedded approaches search for the best features during the training procedure as in the genetic programming algorithm [6]. Feature selection is a discrete optimization problem that attempts to find an optimal value (\"optimum\") of an objective function based on the optimal subset of features. Primarily, optimization problems can optimize the respective set of features, as well as searching for the best values of the hyperparameters. A well-regarded type of optimization algorithms is the metaheuristic algorithms which demonstrated an eminent ability in finding feasible solutions in a reasonable amount of time [7].