Abstract
Current study results on Machine Learning have been published. According to news reporting originating from Rotterdam, Netherlands, by NewsRx correspondents, research stated, “Hyperheuristics are search techniques for selecting, generating, and sequencing (meta)-heuristics to solve challenging optimization problems. They differ from traditional (meta)-heuristics methods, which primarily employ search space-based optimization strategies.” Our news editors obtained a quote from the research from Erasmus University, “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.”
NETL
NSTL
Elsevier