## 1 Introduction  
Autism spectrum disorder (ASD) is a neurological developmental disorder. It affects how people connect and interact with others and how they behave and learn [1]. The symptoms and signs appear when a child is very young. It is a lifelong condition and cannot be cured. Today, ASD is one of the fastest-growing developmental disorders, resulting in many problems, such as school problems related to successful learning, psychological stress within the family, and social isolation. However, early diagnosis can help the family take preliminary and effective steps to ensure the normal life of the patient. It can help providers of healthcare and families of patients by affording the effective therapy and treatment required, thereby reducing the costs associated with delayed diagnosis. On the other hand, many factors can be used to detect ASD cases, including personal and behavioral characteristics, genetic, brain images, and family history. Notwithstanding its genetic causes, ASD is mainly diagnosed utilizing personal and behavioral indicators that are tested in traditional clinical examinations by different specialists during regular visits. However, these traditional clinical methods, which primarily depend on the clinician, are time consuming and cumbersome. Currently, with computer power and big data generated by hospitals such as clinical data, gene expression profiles, and medical imaging, ASD can be automatically predicted and diagnosed in its early stages by using predictive models that use big data sets with ML algorithms, which can improve the life quality of patients and families as well as reduce the financial costs. The personal and behavioral characteristics (PBC) and the gene expression (GE) data are the most available and valuable resources for machine learning (ML) algorithms seeking to discover new and hidden patterns of data to help in ASD prediction, thus helping families to take early steps for treatment. Nevertheless, the high dimensionality of these data makes the prediction process challenging. The feature selection (FS) mechanism can help in reducing the high dimensionality of such datasets, increasing the speed of the classification process, decreasing the cost, and improving the accuracy of the prediction models by selecting the most effective features. Feature selection algorithms [2] aim to choose the most significant features to solve the prediction problems. In general, there are three common types of FS algorithm: filter, wrapper, and hybrid. Due to the potential benefits that can be achieved from automatic ASD classification, research in this field has recently gained much attention. Several methods have been proposed to solve the problem of predicting ASD. However, it is still an open problem and further improvement can be achieved. Bio-inspired algorithms are one of the techniques that can be integrated into the wrapper feature selection method to search globally for the optimal feature subset and improve prediction accuracy [3]. They can be classified as a type of Nature-inspired Computation algorithms that rely on the inspiration of the biological evolution of nature to provide new optimization techniques. A number of researchers have adopted bio-inspired techniques for dealing with the high dimensionality of features, and they have shown high results in improving the diagnosis process of many diseases such as cancer [4]. However, there are few studies in research on ASD prediction using optimized FS algorithms and further investigation in this field is needed. To the best of our knowledge, this is the first study to deal with this problem using four bio-inspired algorithms (GWO, FPA, BA, and ABC). In addition, this is the first study that employed the CNN deep learning approach for ASD GE and PBC datasets. This work aims to enhance the accuracy of early prediction of ASD and the classification performance when dealing with high-dimensional datasets by developing a ML predictive model that is based on an optimized feature selection method using bio-inspired algorithms.