Adversarial Attacks on Evolutionary Algorithms Solving Data-Driven Optimization Problems [research Frontier]

Over the past decades, extensive research has been conducted on adversarial attacks and defense mechanisms in deep learning, particularly in real-world applications such as autonomous vehicles, medical diagnosis, etc. Recent studies have revealed that evolutionary algorithms that solve data-driven optimization problems are also vulnerable to adversarial attacks. However, research endeavors to address adversarial attacks in this context have not yet been initiated. Conducting such attacks is more challenging than those on deep neural networks, primarily due to the absence of defined gradients or loss functions in evolutionary algorithms, which complicates the application of existing attack methods. To address this issue, this paper introduces a novel adversarial attack model targeting evolutionary algorithms solving data-driven optimization problems. To model real-world malicious behaviors, this paper proposes an attack algorithm that develops perturbation cutoff and subpopulation filtering strategies, while also supporting a new sparse operator to ensure the attack’s destructiveness and imperceptibility. The effectiveness of our proposed model and algorithm is demonstrated through experimental evaluation on datasets for both single- and multi-objective optimization problems. Through these experiments, three key questions are addressed: Can a small perturbation of the dataset significantly degrade evolutionary algorithm performance? If so, are robust evolutionary algorithms more resistant to attacks than conventional ones? Furthermore, can the perturbation generated by one evolutionary algorithm also degrade the performance of other evolutionary algorithms? This study emphasizes the importance of evolutionary algorithm safety in real-world applications and provides valuable insights into the future development of trustworthy evolutionary algorithms.