Evolutionary Computation (EC) approaches are inspired by nature and solve optimization problems in a stochastic manner. They can offer a reliable and effective approach to address complex problems in real-world applications. EC algorithms have recently been used to improve the performance of Machine Learning (ML) models and the quality of their results. Evolutionary approaches can be used in all three parts of ML: preprocessing (e.g., feature selection and resampling), learning (e.g., parameter setting, membership functions, and neural network topology), and postprocessing (e.g., rule optimization, decision tree/support vectors pruning, and ensemble learning). This article investigates the role of EC algorithms in solving different ML challenges. We do not provide a comprehensive review of evolutionary ML approaches here; instead, we discuss how EC algorithms can contribute to ML by addressing conventional challenges of the artificial intelligence and ML communities. We look at the contributions of EC to ML in nine sub-fields: feature selection, resampling, classifiers, neural networks, reinforcement learning, clustering, association rule mining, and ensemble methods. For each category, we discuss evolutionary machine learning in terms of three aspects: problem formulation, search mechanisms, and fitness value computation. We also consider open issues and challenges that should be addressed in future work.
Intrusion Detection System (IDS) is an important tool for protecting the Internet of Things (IoT) networks against cyber-attacks. Traditional IDSs can only distinguish between normal and abnormal behaviors. On the other hand, modern techniques can identify the kind of attack so that the appropriate reactions can be carried out against each type of attack. However, these techniques always suffer from the class-imbalance which affects the performance of IDS. In this paper, we propose a cost-sensitive stacked auto-encoder, CSSAE, to deal with class imbalance problem in IDS. CSSAE generates a cost matrix in which a unique cost is assigned to each class based on the distribution of different classes. This matrix is created in the first stage of CSSAE. In the second phase, a two-layer stacked auto-encoder is applied to learn features with better distinguish between the minority and the majority classes. These costs are used in the feature learning of deep learning, where the parameters of the neural network are modified by applying the corresponding costs in the cost function layer. The proposed method is able to perform on both binary-class data and multiclass data. Two well-known KDD CUP 99 and NSL-KDD datasets are used to evaluate the performance of CSSAE. Compared with other IDSs that have not considered class imbalance problem, CSSAE shows better performance in the detection of low-frequency attacks.
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