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Geometric Convergence of Genetic Algorithms Under Tempered Random Restart

Part of: Markov processes Probabilistic methods, simulation and stochastic differential equations

Published online by Cambridge University Press:  14 July 2016

F. Mendivil ,
R. Shonkwiler  and
M. C. Spruill
F. Mendivil*
Affiliation:
Acadia University
R. Shonkwiler*
Affiliation:
Georgia Institute of Technology
M. C. Spruill*
Affiliation:
Georgia Institute of Technology
*
∗Postal address: Mathematics Department, Acadia University, Wolfville, Nova Scotia, Canada. Email address: [email protected]
∗∗Postal address: School of Mathematics, Georgia Institute of Technology, Atlanta, GA 30332-0160, USA.
∗∗Postal address: School of Mathematics, Georgia Institute of Technology, Atlanta, GA 30332-0160, USA.
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Abstract

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Geometric convergence to 0 of the probability that the goal has not been encountered by the nth generation is established for a class of genetic algorithms. These algorithms employ a quickly decreasing mutation rate and a crossover which restarts the algorithm in a controlled way depending on the current population and restricts execution of this crossover to occasions when progress of the algorithm is too slow. It is shown that without the crossover studied here, which amounts to a tempered restart of the algorithm, the asserted geometric convergence need not hold.

Keywords

Nonstationary Markov chainhitting timePerron–Frobeniusdecreasing mutation rate

MSC classification

Secondary: 60J20: Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) 65C05: Monte Carlo methods
Type
Research Article
Information
Journal of Applied Probability , Volume 46 , Issue 4 , December 2009 , pp. 960 - 974
Copyright
Copyright © Applied Probability Trust 2009 

References

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