A Systematic Literature Review on Generative Algorithms and their Impact on Machine Learning

Abstract

This research analyzes the influence and development of Generative Algorithms within the field of Machine Learning (ML), a sphere that is garnering increasing academic and practical interest. The goal is to unveil the state of the art and discern the Generative Algorithms and their impact on ML. A systematic review methodology was adopted, assessing relevant studies published between 2017 and 2023, focusing on Generative Algorithms and their impact on ML. Through the consultation of databases such as Scopus, Web of Science, Science Direct, Springer Link, Google Scholar, and ACM Digital Library, and the application of exclusion criteria presented in the PRISMA Flow Diagram, 62 papers were selected and analyzed. The results highlight a marked presence of research in first-quartile journals, demonstrating the high quality and relevance of the topic. International collaboration emerges as a crucial pillar, with the United States and Canada leading in significant contributions. The most striking conclusions suggest a consolidation of Generative Algorithms as a prominent area of study, with projections towards their integration into novel fields such as quantum computing. The research concludes that global cooperation and institutional support are essential for the progress of ML, emphasizing the importance of adopting collaborative and interdisciplinary approaches in future studies.