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A Simple But Effective Evolutionary Algorithm for Complicated Optimization Problems

A simple but effective evolutionary algorithm is proposed in this paper for solving complicated optimization problems. The new algorithm presents two hybridization operations incorporated with the conventional genetic algorithm. It takes only 4.1% ~ 4.7% number of function evaluations required by the conventional genetic algorithm to obtain global optima for the benchmark functions tested. Application example is also provided to demonstrate its effectiveness. / Singapore-MIT Alliance (SMA)

Identiferoai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/4012
Date01 1900
CreatorsXu, Y.G., Liu, Guirong
Source SetsM.I.T. Theses and Dissertation
Languageen_US
Detected LanguageEnglish
TypeArticle
Format65862 bytes, application/pdf
RelationHigh Performance Computation for Engineered Systems (HPCES);

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