## 1 Introduction  
The process of giving text a label is known as Text Categorization(TC/Text Classification) [1]. When the same comment belongs to many classes (multilabel), this becomes a more challenging task, especially when datasets are imbalanced [2, 3]. Multilabel Arabic Text Classification (ML-ATC) is a crucial issue for the Arabic language because it is widely employed in many different fields, including sentiment analysis, bioinformatics, image classification, and scene classification [4]. There are different transformation methods to handle this task. The first approach is to convert the target variable to a multilabel binarize and then use the OnevsRest with any classification algorithm, such as logistic regression or support vector classifier. The second approach is domain adaption for multilabel classification tasks. In addition, much work has been done for binary and multi-class classification, but multilabel remains extremely difficult, particularly when the Arabic language is taken into account; for instance, one comment contains the categories \"sarcasm,\" \"hate speech,\" and \"fake news\" [5]. Social media is a widely used platform that offers a massive amount of user-generation daily, which is used to learn personal information about users and fight wrong information,such as rumors, gaining crucial information that can aid in decision-making [6]. At this time, one of the hotspots in text mining research is multilabel learning, which has been widely applied to different domains, including image processing, natural language processing, etc. However, multilabel classification of Arabic text is a challenging task due to the complexity and diversity of the Arabic language. On the other side, the overwhelming volume of text data available today makes text processing a challenging task, especially with high dimensions. Selecting the appropriate features is a crucial task and compulsory to achieve by machine learning algorithms in many cases. Dimensionality reduction in the context of text categorization has been successfully achieved using a variety of optimization approaches, including the Genetic Algorithm (GA). However, the performance of these techniques in the multilabel classification of Arabic text is still relatively under-explored [7]. There are distinguished methods available in the literature to select features for the text classification task, such as chi-square [8], mutual information [9], information gain [10], document frequency, and proportional difference. [11; 12]. All these techniques have been used to reduce the dimension and remove words that have a low contribution to enhancing classifier performance. However, in many cases, the performance of the classification task is still poor. Therefore, in this study, a GA-based metaheuristic optimization algorithm is used for feature selection effectively as the advanced binary meta-heuristics models proposed in [13; 14; 15; 16]. This article aims to propose a new model that analyzes and compares the performance of optimization-based feature selection techniques, hybrid representation methods, and ensemble classification techniques for the multilabel classification of Arabic text. Specifically, we will evaluate the effectiveness of the Bag-of-Words (BOW) and Term Frequency-Inverse Document Frequency (TF-IDF) representations. Further, the performance of different classification techniques, such as the Logistic Regression Classifier (LRC), Random Forest Classifier (RFC), and Extra Trees Classifier (ETC) [17; 18], to choose optimal features using a GA is discussed. The ensemble feature selection method helps us to find the optimal feature subset from several feature subsets through an integration method [19]. The contributions of the research work are summarized as follows:  
* Designing an effective Arabic multilabel classification approach using the GA-based metaheuristics feature selection method and ensemble machine learning models.