Professor Graham Kendall is the Provost and CEO of The University of Nottingham Malaysia Campus (UNMC). He is also a Pro-Vice Chancellor of the University of Nottingham.

He is a Director of MyResearch Sdn Bhd, Crops for the Future Sdn Bhd. and Nottingham Green Technologies Sdn Bhd. He is a Fellow of the British Computer Society (FBCS) and a Fellow of the Operational Research Society (FORS).

He has published over 230 peer reviewed papers. He is an Associate Editor of 10 journals and the Editor-in-Chief of the IEEE Transactions of Computational Intelligence and AI in Games.

http://bit.ly/dQPw7T

I am a member of the Automated Scheduling, Optimisation and Planning Research Group

http://bit.ly/eIQ5XC

http://bit.ly/1a34rQJ

A Genetic Programming Hyper-Heuristic Approach for Evolving 2-D Strip Packing Heuristics

http://bit.ly/grTvxk

A hyper-heuristic methodology to generate adaptive strategies for games

http://bit.ly/2qfwz2r

Studying the Effect that a Linear Transformation has on the Time-Series Prediction Ability of an Evolutionary Neural Network

http://bit.ly/eyLaq2

Competitive travelling salesmen problem: A hyper-heuristic approach. Journal of the Operational Research Society, 64 (2): 208-216, 2013.

We introduce a novel variant of the travelling salesmen problem and propose a hyper-heuristic methodology in order to solve it. In a competitive travelling salesmen problem (CTSP), m travelling salesmen are to visit n cities and the relationship between the travelling salesmen is non-cooperative. The salesmen will receive a payoff if they are the first one to visit a city and they pay a cost for any distance travelled. The objective of each salesman is to visit as many unvisited cities as possible, with a minimum travelling distance. Due to the competitive element, each salesman needs to consider the tours of other salesman when planning their own tour. Since equilibrium analysis is difficult in the CTSP, a hyper-heuristic methodology is developed. The model assumes that each agent adopts a heuristic (or set of heuristics) to choose their moves (or tour) and each agent knows that the moves/ tours of all agents are not necessarily optimal. The hyper-heuristic consists of a number of low-level heuristics, each of which can be used to create a move/tour given the heuristics of the other agents, together with a high-level heuristic that is used to select from the low-level heuristics at each decision point. Several computational examples are given to illustrate the effectiveness of the proposed approach.

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@ARTICLE{lk2013, author = {G. Kendall and J. Li},

title = {Competitive travelling salesmen problem: A hyper-heuristic approach},

journal = {Journal of the Operational Research Society},

year = {2013},

volume = {64},

pages = {208--216},

number = {2},

abstract = {We introduce a novel variant of the travelling salesmen problem and propose a hyper-heuristic methodology in order to solve it. In a competitive travelling salesmen problem (CTSP), m travelling salesmen are to visit n cities and the relationship between the travelling salesmen is non-cooperative. The salesmen will receive a payoff if they are the first one to visit a city and they pay a cost for any distance travelled. The objective of each salesman is to visit as many unvisited cities as possible, with a minimum travelling distance. Due to the competitive element, each salesman needs to consider the tours of other salesman when planning their own tour. Since equilibrium analysis is difficult in the CTSP, a hyper-heuristic methodology is developed. The model assumes that each agent adopts a heuristic (or set of heuristics) to choose their moves (or tour) and each agent knows that the moves/ tours of all agents are not necessarily optimal. The hyper-heuristic consists of a number of low-level heuristics, each of which can be used to create a move/tour given the heuristics of the other agents, together with a high-level heuristic that is used to select from the low-level heuristics at each decision point. Several computational examples are given to illustrate the effectiveness of the proposed approach.},

doi = {10.1057/jors.2012.37},

issn = {0160-5682},

keywords = {TSP, Travelling Salesman Problem, Hyper-heuristics, hyperheuristcs, game theory},

owner = {gxk},

timestamp = {2010.10.12},

webpdf = {http://www.graham-kendall.com/papers/lk2013.pdf} }