Advances in Hyper-Heuristics
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.
More details
Persons
Nelishia Pillay is a professor in the Department of Computer Science at the University of Pretoria in Gauteng, South Africa. One of her main areas of research is hyper-heuristics. She has co-authored the book Hyper-Heuristics: Theory and Applications (Springer, 2018) together with Rong Qu. She has previously served as the chair of the IEEE Task Force on Hyper-Heuristics. She has presented 18 tutorials on hyper-heuristics at capstone international conferences in the field. She has authored/co-authored more than 40 peer reviewed research papers on hyper-heuristics.
Rong Qu is a professor in the School of Computer Science at the University of Nottingham, UK. Her main research interests include the modelling and optimization algorithms in scheduling and evolutionary algorithms for optimisation problems. In addition to the previous joint-authored book with Nelishia Pillay on hyper-heuristics, she has published more than 90 papers on intelligent optimisation algorithms at high impact journals since 2000.
Content
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.