Improved Fitness Proportionate Selection-Based Genetic Algorithm

DOI

10.2991/icmit-16.2016.26How to use a DOI?

Keywords

genetic algorithm (GA), fitness proportionate selection, roulette wheel selection, travelling salesman problem (TSP)

Abstract

Genetic algorithms are typical swarm intelligence techniques based on the mechanics of natural selection and natural genetics , which combines artificial survival of the fittest concept with genetic operations abstracted from nature. Since the genetic algorithm has good global search capability, as well as the parallel nature of other advantages, it has been widely used in combinatorial optimization, machine learning, signal processing field, adaptive control and artificial life and so on. It is one of the key technologies related to modern intelligent calculation. Fitness proportionate selection, as a common selection method for GA, is usually implemented with method of roulette wheel selection. In this paper,An improved selection method based on fitness proportionate selection was presented. Computational results show that the method which proposed in this paper improved the result precision and better astringency by solving TSP problem.

Copyright

© 2016, 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/).

Download article (PDF)

TY  - CONF
AU  - Fengrui Yu
AU  - Xueliang Fu
AU  - Honghui Li
AU  - Gaifang Dong
PY  - 2016/04
DA  - 2016/04
TI  - Improved Fitness Proportionate Selection-Based Genetic Algorithm
BT  - Proceedings of the 2016 3rd International Conference on Mechatronics and Information Technology
PB  - Atlantis Press
SP  - 136
EP  - 140
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmit-16.2016.26
DO  - 10.2991/icmit-16.2016.26
ID  - Yu2016/04
ER  -