## 1 Introduction  
[PERSON] defined the notion of an outlier as an observation that diverges greatly from the other observations so as to trigger suspicions that it was generated by a separate mechanism [1]. Detecting outliers is the process of determining whether the data have input errors and detecting unusual patterns that are substantially different from the bulk of the data. The detection of outliers is a critical step in data analysis that needs to be prioritized with meticulous attention as data containing outliers can negatively influence the accuracy of a model [2], increase the likelihood of false positives [3], lead to wrong decisions [4],and make the model computationally costly [5]. Thus, it is imperative to pay close consideration to the outliers in the data and analyze their causes to provide opportunities to discover problems and strengthen the decision-making [6]. Outlier detection has been extensively used in a multitude of fields, such as fraud detection [7], network intrusion detection [8], medical diagnosis [9], video surveillance [10], and fault diagnosis [11] etc. As high-dimensional data continues to grow exponentially, it enables data sharing and provides a solid basis for analysis and prediction. Nonetheless, it poses great challenges to the processing and detection of outlier detection methods. One of the most notable problems is the curse of dimensionality [12]. The large number of dimensions for high-dimensional data makes the concepts of distance and proximity calculations in high-dimensional space unreliable. Therefore, they are unsuitable to be used as an effective indicator for abnormality measurement. The sparsity between the dimensions makes almost every point depict an anomalous characteristic. In addition, it is exceedingly expensive to search through the full space of high-dimensional data to obtain abnormal subspace features. Furthermore, some existing outlier detection methods are prone to a high false positive rate. That is, the major portion of the detected outliers are not actually anomalies in the actual application domains. Finally, it is becoming increasingly complicated but essential to provide interpretations for outlier detection results in high-dimensional space as a consequence of the large number of features. In order to improve the interpretation of high-dimensional outliers and alleviate the challenges related to high-dimensional data, we propose an unsupervised outlier detection method for high-dimensional datasets based on variational autoencoders and genetic algorithms (VAEGA). The model is divided into two parts. In the first part, we build a variational encoder and decoder layer with a neural network. This is attained by feeding normal unlabeled data as input for training and iteratively learning the best encoding-decoding strategy to effectively represent the high-dimensional data distribution. Using the probability distribution of the VAE's hidden space, hidden vectors are randomly sampled to reconstruct the data. Next, the reconstruction error of the data is calculated, and outliers are identified among the test samples whose values transcend the model's threshold value. Even though the variational encoder and decoder layers are capable of effectively detecting the outliers, despite the curse of dimensionality, the results are prone to false positives and lack of desired interpretability. In order to address and propose a solution to such a defect, we apply a genetic algorithm (GA) in the second part of our model. We train the genetic algorithm (GA) layer, and it searches for the abnormal subspace of the outliers that we observed in the first part. Each subspace has a fitness function that measures the degree of abnormality of each outlier detected in that subspace. The abnormal subspace of the training dataset is utilized to effectively filter false positives. The top abnormal subspace also provides insightful and explanatory interpretations for the context where the outliers are detected.