Title :
A hybrid evolutionary algorithm for promoter recognition
Author :
Lan Tao ; Ning Fan ; Zexuan Zhu
Author_Institution :
Coll. of Comput. & Software, Shenzhen Univ., Shenzhen, China
Abstract :
The aim of this study is to identify the smallest possible set of features and optimal model parameters for promoter recognition. Particularly, we propose a novel hybrid evolutionary algorithm, which integrates Markov blanket-embedded genetic algorithm (MBEGA), comprehensive learning particle swarm optimize (CLPSO), and support vector machine (SVM) as a whole. This method adopts MBEGA for promoter feature selection while employs CLPSO to optimize the parameters of the promoter identification model. Empirical results on the eukaryotic promoter database (EPD) suggest that, our proposed approach is able to obtain better or competitive classification accuracy than other methods and it is effective and efficient in eliminating irrelevant and redundant features in training process.
Keywords :
Markov processes; evolutionary computation; genetic algorithms; learning (artificial intelligence); medical computing; particle swarm optimisation; support vector machines; MBEGA; Markov blanket-embedded genetic algorithm; SVM; competitive classification accuracy; comprehensive learning particle swarm optimisation; eukaryotic promoter database; hybrid evolutionary algorithm; optimal model parameters; promoter feature selection; promoter identification model; promoter recognition; support vector machine; training process; CLPSO; MBEGA; feature optimal selection; promoter classification;
Conference_Titel :
Biomedical Engineering and Informatics (BMEI), 2012 5th International Conference on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4673-1183-0
DOI :
10.1109/BMEI.2012.6512883