G
Graham Kendall
Researcher at University of Nottingham Malaysia Campus
Publications - 304
Citations - 14989
Graham Kendall is an academic researcher from University of Nottingham Malaysia Campus. The author has contributed to research in topics: Heuristics & Heuristic. The author has an hindex of 60, co-authored 292 publications receiving 13452 citations. Previous affiliations of Graham Kendall include Universities UK & University of Bradford.
Papers
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Journal ArticleDOI
Hyper-heuristics: a survey of the state of the art
Edmund K. Burke,Michel Gendreau,Matthew Hyde,Graham Kendall,Gabriela Ochoa,Ender Özcan,Rong Qu +6 more
TL;DR: A critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas are presented.
BookDOI
Genetic and Evolutionary Computation -- GECCO-2003
Erick Cantú-Paz,James A. Foster,Kalyanmoy Deb,Lawrence Davis,Rajkumar Roy,Una-May O'Reilly,Hans-Georg Beyer,Russell Standish,Graham Kendall,Stewart W. Wilson,Mark Harman,Joachim Wegener,Dipankar Dasgupta,Mitch A. Potter,Alan C. Schultz,Kathryn A. Dowsland,Natasha Jonoska,Julian F. Miller +17 more
TL;DR: This work extends the application of CPSO to the dynamic problem by considering a bi-modal parabolic environment of high spatial and temporal severity, and suggests that charged swarms perform best in the extreme cases, but neutral swarms are better optimizers in milder environments.
Book ChapterDOI
Hyper-Heuristics: An Emerging Direction in Modern Search Technology
TL;DR: This chapter introduces and overviews an emerging methodology in search and optimisation called hyperheuristics, which aims to raise the level of generality at which optimisation systems can operate and will lead to more general systems that are able to handle a wide range of problem domains.
Book
Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques
Edmund K. Burke,Graham Kendall +1 more
TL;DR: The first edition of Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques was originally put together to offer a basic introduction to the various search and optimization techniques that students might need to use during their research, and this new edition continues this tradition.
Journal ArticleDOI
A Tabu-Search Hyperheuristic for Timetabling and Rostering
TL;DR: It is demonstrated that this tabu-search hyperheuristic is an easily re-usable method which can produce solutions of at least acceptable quality across a variety of problems and instances and is fundamentally more general than state-of-the-art problem-specific techniques.