Runtime Analysis of OneMax Problem in Genetic Algorithm

DOI

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 Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Yifei Du
AU  - QinLian Ma
AU  - Makoto Sakamoto
AU  - Hiroshi Furutani
AU  - Yu-an Zhang
PY  - 2014
DA  - 2014/12/15
TI  - Runtime Analysis of OneMax Problem in Genetic Algorithm
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 225
EP  - 230
VL  - 1
IS  - 3
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.2014.1.3.12
DO  - 10.2991/jrnal.2014.1.3.12
ID  - Du2014
ER  -