Paper Info
Title
A mathematical analysis of the long-run behavior of genetic algorithms for social modeling
Abstract
We present a mathematical analysis of the long-run behavior of genetic algorithms (GAs) that are used for modeling social phenomena. Our analysis relies on commonly used mathematical techniques in the field of evolutionary game theory. We make a number of assumptions in our analysis, the most important one being that the mutation rate is positive but infinitely small. Given our assumptions, we derive results that can be used to calculate the exact long-run behavior of a GA. Using these results, the need to rely on computer simulations can be avoided. We also show that if the mutation rate is infinitely small the crossover rate has no effect on the long-run behavior of a GA. To demonstrate the usefulness of our mathematical analysis, we replicate a well-known study by Axelrod in which a GA is used to model the evolution of strategies in iterated prisoner’s dilemmas. The theoretically predicted long-run behavior of the GA turns out to be in perfect agreement with the long-run behavior observed in computer simulations. Also, in line with our theoretically informed expectations, computer simulations indicate that the crossover rate has virtually no long-run effect. Some general new insights into the behavior of GAs in the prisoner’s dilemma context are provided as well.
Year
DOI
Venue
2012
10.1007/s00500-012-0804-x
Soft Comput.
Keywords
Field
DocType
Genetic algorithm,Long-run behavior,Social modeling,Economics,Evolutionary game theory
Mathematical optimization,Mutation rate,Computer science,Mathematical analysis,Crossover rate,Artificial intelligence,Evolutionary game theory,Iterated function,Social modeling,Replicate,Genetic algorithm,Machine learning
Journal
Volume
Issue
ISSN
16
6
1432-7643
Citations 
PageRank 
References 
1
0.35
15
Authors
2
Name
Order
Citations
PageRank
Ludo Waltman12236105.47
Nees Jan van Eck2574.47