Graham Kendall
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Professor Graham Kendall

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.

News

I have published some papers on timetabling.
http://bit.ly/hSGAhZ
I have published some papers on timetabling.
http://bit.ly/hSGAhZ

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

EURO 2009 Conference

Publication(s)

A Hybrid Evolutionary Approach to the Nurse Rostering Problem
http://bit.ly/ey147Y
Chapter 4: An Investigation of Automated Planograms Using a Simulated Annealing Based Hyper-Heuristic
http://bit.ly/1cJv7H4
A New Approach to Packing Non-Convex Polygons Using the No Fit Polygon and Meta-Heuristic and Evolutionary Algorithms
http://bit.ly/eMYCKs
Population based Monte Carlo tree search hyper-heuristic for combinatorial optimization problems
http://bit.ly/1IIArdQ

Graham Kendall: Details of Requested Publication


Citation

Sastry, K; Goldberg, D and Kendall, G Chapter 4: Genetic Algorithms. In Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, pages 93-117, Springer, 2014.


Abstract

Genetic algorithms (GAs) are search methods based on principles of natural selection and genetics (Fraser 1957; Bremermann 1958; Holland 1975). We start with a brief introduction of simple GAs and the associated terminologies. GAs encode the decision variables of a search problem into finite-length strings of alphabets of certain cardinality. The strings which are candidate solutions to the search problem are referred to as chromosomes, the alphabets are referred to as genes and the values of genes are called alleles. For example, in a problem such as the traveling salesman problem (TSP), a chromosome represents a route, and a gene may represent a city. In contrast to traditional optimization techniques, GAs work with coding of parameters, rather than the parameters themselves.


pdf

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doi

The doi for this publication is 10.1007/978-1-4614-6940-7_4 You can link directly to the original paper, via the doi, from here

What is a doi?: A doi (Document Object Identifier) is a unique identifier for sicientific papers (and occasionally other material). This provides direct access to the location where the original article is published using the URL http://dx.doi/org/xxxx (replacing xxx with the doi). See http://dx.doi.org/ for more information



URL

The URL for additional information is http://link.springer.com/chapter/10.1007/978-1-4614-6940-7_4

The URL is only provided if there is additional information that might be useful. For example, where the entry is a book chapter, the URL might link to the book itself.


Bibtex

@INBOOK{sgk2014, chapter = {Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques},
pages = {93--117},
title = {Chapter 4: Genetic Algorithms},
publisher = {Springer},
year = {2014},
editor = {E.K. Burke and G. Kendall},
author = {K. Sastry and D. Goldberg and G. Kendall},
abstract = {Genetic algorithms (GAs) are search methods based on principles of natural selection and genetics (Fraser 1957; Bremermann 1958; Holland 1975). We start with a brief introduction of simple GAs and the associated terminologies. GAs encode the decision variables of a search problem into finite-length strings of alphabets of certain cardinality. The strings which are candidate solutions to the search problem are referred to as chromosomes, the alphabets are referred to as genes and the values of genes are called alleles. For example, in a problem such as the traveling salesman problem (TSP), a chromosome represents a route, and a gene may represent a city. In contrast to traditional optimization techniques, GAs work with coding of parameters, rather than the parameters themselves.},
doi = {10.1007/978-1-4614-6940-7_4},
keywords = {genetic algorithm, meta-heuristics, serach methodologies, metaheuristics},
owner = {gxk},
timestamp = {2010.12.11},
url = {http://link.springer.com/chapter/10.1007/978-1-4614-6940-7_4},
webpdf = {http://www.graham-kendall.com/papers/sgk2014.pdf} }