Authors
Yifei Du, QinLian Ma, Makoto Sakamoto, Hiroshi Furutani, Yu-an Zhang
Corresponding Author
Yifei Du
Available Online 15 December 2014.
DOI
https://doi.org/10.2991/jrnal.2014.1.3.12How to use a DOI?
Keywords
genetic algorithms, schema theory, OneMax problem, Markov model, convergence time
Abstract
Genetic algorithms (GAs) are stochastic optimization techniques, and we have studied the effects of stochastic fluctuation in the process of GA evolution. A mathematical study was carried out for GA on OneMax function within the framework of Markov chain model. We obtained the steady state solution, which represents a distribution of the first order schema frequency. We treated the task of estimating convergence time of the Markov chain for OneMax problem, and studied the effects of mutation probability and string length on the convergence time.

Copyright
© 2013, the Authors. Published by ALife Robotics Corp. Ltd.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).