An improved adaptive genetic algorithm considering the overall process solving capacity

DOI

10.2991/icmse-15.2015.188How to use a DOI?

Keywords

Adaptive Genetic Algorithm (AGA); overall evolutionary capacity; solving stability

Abstract

To solve the problems of easily falling into local optimal solution and solving process instability for adaptive genetic algorithms, an improved adaptive genetic algorithm is presented by improving the crossover and mutation probability calculation method, which balances the solving stability and reliability in the early and late process. The crossover and mutation probability of the superiority individuals should be relatively small in the evolutionary process. But they can not be zero so as to avoid early entering into local optimal solutions since the superiority individuals can not make the crossover and mutation operation and produce new individuals in early evolutionary process. The worse individuals should have a greater crossover and mutation probability in the evolutionary process in order to increase the number of new individuals and prevent premature entry into the local optimal solution. The case analysis demonstrates that this algorithm has better ability of global optimization and stability.

Copyright

© 2015, 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  - CONF
AU  - Zhi-Jie Liu
AU  - Xiao-Qian Zhao
AU  - Xiao-Long Liu
AU  - Xiao-Yu Liu
PY  - 2015/12
DA  - 2015/12
TI  - An improved adaptive genetic algorithm considering the overall process solving capacity
BT  - Proceedings of the 2015 6th International Conference on Manufacturing Science and Engineering
PB  - Atlantis Press
SP  - 1034
EP  - 1039
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmse-15.2015.188
DO  - 10.2991/icmse-15.2015.188
ID  - Liu2015/12
ER  -