A novel and accelerated genetic algorithm

Authors:
Huang Bao-Juan;Zhuang Jian;Yu De-Hong
Affiliations:
School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China;School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China;School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China
Venue:
WSEAS Transactions on Systems and Control
Year:
2008

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Abstract

Genetic algorithm (GA) is very helpful when the developer does not have precise domain expertise, because GA possesses the ability to explore and learn from their domain. At present, the research of GA mainly focuses on the three operators and devotes to improve the algorithm efficiency and avoid premature convergence. This paper presents a cycle mutation operator and a novel selection operator; accordingly, an improved cycle mutation genetic algorithm (ICMGA) is schemed, The experimental results compared with other genetic algorithms validate the performance of this algorithm, such as the exploration ability in search space, the stabilization and calculation speed, are all superior to other algorithms, and ICMGA is not sensitive to the initial population distribution.