What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Law
- Mathematical optimization
Graham Kendall mainly investigates Heuristics, Mathematical optimization, Artificial intelligence, Heuristic and Hyper-heuristic.
His work deals with themes such as Theoretical computer science, Bin packing problem, Problem domain, Vehicle routing problem and Scheduling, which intersect with Heuristics.
He combines subjects such as Range and Algorithm with his study of Mathematical optimization.
His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Machine learning.
His studies in Heuristic integrate themes in fields like Mount and Industrial engineering.
His work in Hyper-heuristic addresses subjects such as Tabu search, which are connected to disciplines such as Adaptation.
His most cited work include:
- Hyper-heuristics: a survey of the state of the art (703 citations)
- Hyper-Heuristics: An Emerging Direction in Modern Search Technology (546 citations)
- Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques (545 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Mathematical optimization, Artificial intelligence, Heuristics, Heuristic and Machine learning.
His biological study spans a wide range of topics, including Timetabling problem and Benchmark.
In Timetabling problem, Graham Kendall works on issues like Operations research, which are connected to Quality.
His Evolutionary computation, Evolutionary algorithm, Artificial neural network, Genetic programming and Selection study are his primary interests in Artificial intelligence.
His study explores the link between Heuristics and topics such as Scheduling that cross with problems in Football.
As part of his studies on Heuristic, Graham Kendall frequently links adjacent subjects like Heuristic.
He most often published in these fields:
- Mathematical optimization (36.96%)
- Artificial intelligence (32.30%)
- Heuristics (19.25%)
What were the highlights of his more recent work (between 2014-2021)?
- Mathematical optimization (36.96%)
- Artificial intelligence (32.30%)
- Benchmark (11.80%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Mathematical optimization, Artificial intelligence, Benchmark, Hyper-heuristic and Timetabling problem.
Graham Kendall has researched Artificial intelligence in several fields, including Machine learning and Sequential game, Combinatorial game theory.
He interconnects Algorithm, Differential evolution and Solver in the investigation of issues within Benchmark.
His Hyper-heuristic research is multidisciplinary, incorporating perspectives in Test suite, Selection and Heuristic.
His study in Heuristic is interdisciplinary in nature, drawing from both Probabilistic logic, Heuristics and Fuzzy clustering.
As part of one scientific family, Graham Kendall deals mainly with the area of Timetabling problem, narrowing it down to issues related to the Great Deluge algorithm, and often Operations research.
Between 2014 and 2021, his most popular works were:
- Automatic Design of a Hyper-Heuristic Framework With Gene Expression Programming for Combinatorial Optimization Problems (72 citations)
- A Dynamic Multiarmed Bandit-Gene Expression Programming Hyper-Heuristic for Combinatorial Optimization Problems (67 citations)
- An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems (66 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Law
- Algorithm
Graham Kendall mainly focuses on Mathematical optimization, Hyper-heuristic, Heuristic, Benchmark and Artificial intelligence.
His research in Mathematical optimization tackles topics such as Timetabling problem which are related to areas like Combinatorial optimization.
His Hyper-heuristic research integrates issues from Test suite, Selection and Heuristics.
His Heuristics study combines topics in areas such as Optimization problem, Vehicle routing problem, Bin packing problem and Flow shop scheduling.
His Benchmark research is multidisciplinary, incorporating elements of Artificial bee colony algorithm, State and Multi population.
His Artificial intelligence research focuses on subjects like Machine learning, which are linked to Estimation of distribution algorithm.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
