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 a few papers on Sports Scheduling.
http://bit.ly/gVaUqT
Help solve Santa's logistics problems
http://bit.ly/1DXreuW

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Informing publishers of my tweeting activities

Publication(s)

A Variable Neighborhood Descent Search Algorithm for Delay-Constrained Least-Cost Multicast Routing
http://bit.ly/h1puUB
The Cross-domain Heuristic Search Challenge - An International Research Competition
http://bit.ly/1a2VfMs
Journals Rankings: Buyer Beware
http://bit.ly/1iaSVYu
An Ant Based Hyper-heuristic for the Travelling Tournament Problem
http://bit.ly/gPYAJl

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 97-125, Springer, 2005.


Abstract

There is no abstract to this chapter, but this is the first few paragraphs. Genetic algorithms (GAs) are search methods based on principles of natural selection and genetics. We start with a brief introduction to simple genetic algorithms and associated terminology. 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, 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. To evolve good solutions and to implement natural selection, we need ameasure for distinguishing good solutions from bad solutions. The measure could be an objective function that is a mathematical model or a computer simulation, or it can be a subjective function where humans choose better solutions over worse ones. In essence, the fitness measure must determine a candidate solutionís relative fitness, which will subsequently be used by the GA to guide the evolution of good solutions. Another important concept of GAs is the notion of population. Unlike traditional search methods, genetic algorithms rely on a population of candidate solutions. The population size, which is usually a user-specified parameter, is one of the important factors affecting the scalability and performance of genetic algorithms. For example, small population sizes might lead to premature convergence and yield substandard solutions. On the other hand, large population sizes lead to unnecessary expenditure of valuable computational time.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1007/0-387-28356-0_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://www.springerlink.com/content/978-0-387-23460-1

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{sgk2005, chapter = {Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques},
pages = {97--125},
title = {Chapter 4: Genetic Algorithms},
publisher = {Springer},
year = {2005},
editor = {E.K. Burke and G. Kendall},
author = {K. Sastry and D. Goldberg and G. Kendall},
abstract = {There is no abstract to this chapter, but this is the first few paragraphs. Genetic algorithms (GAs) are search methods based on principles of natural selection and genetics. We start with a brief introduction to simple genetic algorithms and associated terminology. 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, 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. To evolve good solutions and to implement natural selection, we need ameasure for distinguishing good solutions from bad solutions. The measure could be an objective function that is a mathematical model or a computer simulation, or it can be a subjective function where humans choose better solutions over worse ones. In essence, the fitness measure must determine a candidate solutionís relative fitness, which will subsequently be used by the GA to guide the evolution of good solutions. Another important concept of GAs is the notion of population. Unlike traditional search methods, genetic algorithms rely on a population of candidate solutions. The population size, which is usually a user-specified parameter, is one of the important factors affecting the scalability and performance of genetic algorithms. For example, small population sizes might lead to premature convergence and yield substandard solutions. On the other hand, large population sizes lead to unnecessary expenditure of valuable computational time.},
doi = {10.1007/0-387-28356-0_4},
keywords = {genetic algorithm, meta-heuristics, serach methodologies, metaheuristics},
owner = {gxk},
timestamp = {2010.12.11},
url = {http://www.springerlink.com/content/978-0-387-23460-1},
webpdf = {http://www.graham-kendall.com/papers/sgk2005.pdf} }