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2009 Third International Conference on Genetic and Evolutionary Computing
Improved Genetic Algorithm for Aircraft Departure Sequencing Problem
Guilin, China
October 14-October 17
ISBN: 978-0-7695-3899-0
ASCII Text
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Lai-jun Wang, Da-wei Hu, Rui-zi Gong, "Improved Genetic Algorithm for Aircraft Departure Sequencing Problem," Genetic and Evolutionary Computing, International Conference on, pp. 35-38, 2009 Third International Conference on Genetic and Evolutionary Computing, 2009.
 
 
BibTex
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@article{ 10.1109/WGEC.2009.125,
author = {Lai-jun Wang and Da-wei Hu and Rui-zi Gong},
title = {Improved Genetic Algorithm for Aircraft Departure Sequencing Problem},
journal ={Genetic and Evolutionary Computing, International Conference on},
volume = {0},
year = {2009},
isbn = {978-0-7695-3899-0},
pages = {35-38},
doi = {http://doi.ieeecomputersociety.org/10.1109/WGEC.2009.125},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Genetic and Evolutionary Computing, International Conference on
TI - Improved Genetic Algorithm for Aircraft Departure Sequencing Problem
SN - 978-0-7695-3899-0
SP35
EP38
A1 - Lai-jun Wang,
A1 - Da-wei Hu,
A1 - Rui-zi Gong,
PY - 2009
KW - departure sequencing
KW - adaptive genetic algorithms
KW - total probability crossover
KW - wake vortex separation
VL - 0
JA - Genetic and Evolutionary Computing, International Conference on
ER -
 
Optimization model is build for solving the aircraft departure sequencing problem in this paper first. Then, an improved genetic algorithm (GA) using symbolic coding is proposed, where a type of total probability crossover and big probability mutation are performed. In this way, the evolutionary policy of Particle Swarm Optimization (PSO) is absorbed into the improved GA, which reduces the complexity and enhance the efficiency greatly. Last, a simulation program using basic GA, adaptive GA, and improved GA is performed. The simulation result shows that the model is effective and Improved GA has better performance than Basic GA or Adaptive GA.
Index Terms:
departure sequencing, adaptive genetic algorithms, total probability crossover, wake vortex separation
Citation:
Lai-jun Wang, Da-wei Hu, Rui-zi Gong, "Improved Genetic Algorithm for Aircraft Departure Sequencing Problem," wgec, pp.35-38, 2009 Third International Conference on Genetic and Evolutionary Computing, 2009
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