Title :
Research on coding technology based on semantic for feature parameter optimization
Author :
Jin, Ying-hao ; Sun, Li-quan
Author_Institution :
Students´´ Affairs Div., Tonghua Normal Univ., Tonghua, China
Abstract :
To improve the efficiency of genetic algorithm for feature parameter optimization, a new method is presented. It determines the range of feature parameters by the availability of model, creates the coding structure of individual by model features and coding and decoding individual by features´ semantic. This method can not only improve the efficiency of coding and decoding, but also increase the evolution speed of populations. Experiments on computer show that this new method is more adaptable and practicable.
Keywords :
computational geometry; decoding; feature extraction; genetic algorithms; parameter estimation; coding technology; decoding; feature parameter determination; feature parameter optimization; genetic algorithm; semantic feature modeling; Adaptation models; Computational modeling; Design automation; Educational institutions; Encoding; Optimization; Semantics; coding; feature parameter optimization; genetic algorithm; representation of semantic; semantic feature modeling;
Conference_Titel :
Electronics, Communications and Control (ICECC), 2011 International Conference on
Conference_Location :
Ningbo
Print_ISBN :
978-1-4577-0320-1
DOI :
10.1109/ICECC.2011.6067620
