DocumentCode :
2820066
Title :
A hierarchical Pareto dominance based multi-objective approach for the optimization of gene regulatory network models
Author :
Xinye Cai ; Zhenzhou Hu ; Das, S. ; Welch, S.M.
Author_Institution :
Coll. of Comput. Sci. & Technol., Nanjing Univ. of Aeronaut. & Astronaut., Nanjing, China
fYear :
2012
fDate :
10-15 June 2012
Firstpage :
1
Lastpage :
6
Abstract :
In this paper, a hierarchical Pareto dominance based multi-objective evolutionary approach is proposed for the optimization of gene regulatory network models. The approach is presented based on the neglected observations in GRN optimization that (i) structural dependencies exist among objectives; and (ii) some objectives may be more important than others. The hierarchical Pareto dominance is able to reduce the number of objectives during optimization process and increase the selection pressure to relieve the many objective problem. The proposed hierarchical Pareto dominance based multi-objective approach is verified and compared with classical Pareto dominance based algorithm NSGAII on the gene regulatory network optimization problem. The results obtained indicate that the presented approach has great performance when no noise exist. Also it shows superior results compared to NSGAII.
Keywords :
Pareto optimisation; biology computing; evolutionary computation; genetics; GRN optimization; NSGAII; gene regulatory network model optimization; genetic networks; hierarchical Pareto dominance based multiobjective evolutionary approach; Data models; Genetics; Mathematical model; Noise; Noise level; Optimization; Switches;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation (CEC), 2012 IEEE Congress on
Conference_Location :
Brisbane, QLD
Print_ISBN :
978-1-4673-1510-4
Electronic_ISBN :
978-1-4673-1508-1
Type :
conf
DOI :
10.1109/CEC.2012.6256431
Filename :
6256431
Link To Document :
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