DocumentCode :
3272077
Title :
Notice of Retraction
Adaptive Genetic Algorithm Based on Population Diversity
Author :
Liu Xinping ; Liu Ying
Author_Institution :
Coll. of Comput. & Commun. Eng., China Univ. of Pet., Dongying, China
Volume :
2
fYear :
2009
fDate :
15-17 May 2009
Firstpage :
510
Lastpage :
512
Abstract :
Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

Population diversity is the precondition of population evolution. Premature convergence is a problem in genetic algorithm. It is seriously influenced by the distribution properties of initial population. So an adaptive genetic algorithm based on diversity is proposed in this paper. It uses the max-min distance means and makes various individuals maintain some certain Hamming distance to produce good population distribution. Simultaneously the genetic operators are adaptively determined according to the population diversity and individual fitness. The diversity of population can be effectively maintained and a global optimal solution can be quickly obtained using the proposed method. Finally, four representative test functions are chosen to test the improved adaptive genetic algorithm´s capability. The simulation and comparison results show the validity of this algorithm.
Keywords :
convergence; genetic algorithms; minimax techniques; Hamming distance; adaptive genetic algorithm; genetic operator; max-min distance; population distribution; population diversity; population evolution; premature convergence; Application software; Convergence; Educational institutions; Equations; Evolution (biology); Genetic algorithms; Genetic engineering; Hamming distance; Information technology; Testing; genetic algorithm; genetic operators; initial population; max-min distance means; population diversity;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Technology and Applications, 2009. IFITA '09. International Forum on
Conference_Location :
Chengdu
Print_ISBN :
978-0-7695-3600-2
Type :
conf
DOI :
10.1109/IFITA.2009.262
Filename :
5231387
Link To Document :
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