A survey on batch training in genetic programming

Abstract In Machine Learning (ML), the use of subsets of training data, referred to as batches, rather than the entire dataset, has been extensively researched to reduce computational costs, improve model efficiency, and enhance algorithm generalization. Despite extensive research, a clear definition and consensus on what constitutes batch training have yet to be reached, leading to a fragmented body of literature that could otherwise be seen as different facets of a unified methodology. To address this gap, we propose a theoretical redefinition of batch training, creating a clearer and broader overview that integrates diverse perspectives. We then apply this refined concept specifically to Genetic Programming (GP). Although batch training techniques have been explored in GP, the term itself is seldom used, resulting in ambiguity regarding its application in this area. This review seeks to clarify the existing literature on batch training by presenting a new and practical classification system, which we further explore within the specific context of GP. We also investigate the use of dynamic batch sizes in ML, emphasizing the relatively limited research on dynamic or adaptive batch sizes in GP compared to other ML algorithms. By bringing greater coherence to previously disjointed research efforts, we aim to foster further scientific exploration and development. Our work highlights key considerations for researchers designing batch training applications in GP and offers an in-depth discussion of future research directions, challenges, and opportunities for advancement.