DocumentCode
2918406
Title
Exploring the use of ancestry as a unified network model of finite population evolution
Author
Whigham, Peter A. ; Dick, Grant
Author_Institution
Inf. Sci. Dept., Otago Univ., Dunedin
fYear
2008
fDate
1-6 June 2008
Firstpage
3734
Lastpage
3740
Abstract
The evolution of a population is determined by many factors, including the geographic separation of individuals in the population (spatial structure), parent selection via assortative mating (biasing who breeds with whom), environmental gradients, founder effects, disturbance, selection, stochastic effects characterised as genetic drift and so on. Ultimately the interest in studying a population of organisms is about characterising parent selection over time. This paper will examine the evolution of a population under the neutral conditions of genetic drift and for a simple selection model. For drift two conditions are considered: the first is for a range of spatial (geographic) constraints defined by a network; the second is through the use of a tagging system that models assortative mate selection. A simple selection model for the OneMax problem is used to illustrate the response of a population to selection pressure. An ancestry network is constructed representing the shared parent interactions over time. This structure is analyzed as a method for characterising the interactions of a population. The approach demonstrates a unified model to characterise population dynamics, independent of the underlying evolutionary constraints.
Keywords
evolutionary computation; OneMax problem; ancestry network; assortative mate selection; evolutionary constraints; finite population evolution; genetic drift; parent selection; population evolution; simple selection model; tagging system; unified network model; Biological system modeling; Evolution (biology); Evolutionary computation; Genetic mutations; Mathematical model; Organisms; Pattern analysis; Phylogeny; Stochastic processes; Tagging;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on
Conference_Location
Hong Kong
Print_ISBN
978-1-4244-1822-0
Electronic_ISBN
978-1-4244-1823-7
Type
conf
DOI
10.1109/CEC.2008.4631303
Filename
4631303
Link To Document