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Full Description
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.
Contents
Chapter 1: Introduction.- Chapter 2: Generalization Levels of Hyper-Heuristics.- Chapter 3: Evaluation of Hyper-Heuristic Performance.- Chapter 4 - Standardization of Hyper-Heuristics.- Chapter 5: Automated Design Using Hyper-Heuristics.- Chapter 6: Machine Learning in Hyper-Heuristics.- Chapter 7: Cross-Domain Hyper-Heuristics Revisited.- Chapter 8: Hybrid Hyper-Heuristics.- Chapter 9: Hyper-Heuristics for Continuous Optimization.- Chapter 10: Explainable Hyper-Heuristics.- Chapter 11: Automated Design of Hyper-Heuristics.- Chapter 12: Transfer Learning in Hyper-Heuristics.- Chapter 13: Future Research Directions.- Chapter 14: Conclusions.
