Advances in Hyper-Heuristics
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Beschreibung
The field of hyper-heuristics has been developing rapidly over the years with a number of new advancements in the field. The book firstly examines the different levels of generality that can be attained by a hyper-heuristic and provides a standardization for hyper-heuristics. The book investigates a further level of generality in hyper-heuristics across discrete and continuous optimization. The concept of learning within hyper-heuristics is then reviewed. The use of hyper-heuristics for the automated design of machine learning and search algorithms as well as the automated design of hyper-heuristics and hybrid hyper-heuristics is examined. An overview of the use of approaches not previously employed by hyper-heuristics, such as neural networks, is given. Recent trends in computational intelligence, namely, transfer learning and explainable artificial intelligence, are reported in the context of hyper-heuristics. Recent applications of hyper-heuristics in areas such multi-objective optimization and search-based software engineering are also presented.
This book is suitable for postgraduate students, researchers, and practitioners who are interested in evolutionary computing, artificial intelligence, or operations research.
Features recent developments such as standardization, generality and evaluation for hyper-heuristics Provides an overview of advances in hyper-heuristics: automated design, transfer learning & explainable hyper-heuristic Highlights future research directions in the rapidly evolving field of hyper-heuristics
Autor*in
Nelishia Pillay
Themen in »Advances in Hyper-Heuristics«
hyper-heuristics standardization automated algorithm design multi-objective optimization generalization