"Due to the remarkable performance of hyper-heuristics in multi-objective and machine learning-based optimization, there has been an increasing interest in this field. With a fresh perspective, our work extends the current taxonomy and presents an overview of the most significant hyper-heuristic studies of the last two decades. Four categories under which we analyze hyperheuristics are selection hyper-heuristics (including machine learning techniques), low-level heuristics, target optimization problems, and parallel hyper-heuristics."
According to the report, the research concluded: "Future research prospects, trends, and prospective fields of study are also explored."
The research has been published in www.journals.elsevier.com/computers-and-industrial-engineering/