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Efficient Numerical Optimization Algorithm Based on New Real-Coded Genetic Algorithm, AREX + JGG, and Application to the Inverse Problem in Systems Biology
Department of Bioinformatics, Graduate School of Systems Life Sciences, Kyushu University, Fukuoka, Japan
Faculty of Inernational Communications, Fukuoka International University, Fukuoka, Japan
Department of Systems Bioscience for Drug Discovery, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Japan
Graduate School of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Tokyo, Japan
Department of Bioinformatics, Graduate School of Systems Life Sciences, Kyushu University, Fukuoka, Japan
- 1 Department of Bioinformatics, Graduate School of Systems Life Sciences, Kyushu University, Fukuoka, Japan
- 2 Faculty of Inernational Communications, Fukuoka International University, Fukuoka, Japan
- 3 Department of Systems Bioscience for Drug Discovery, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Japan
- 4 Graduate School of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Tokyo, Japan
- 5 Department of Bioinformatics, Graduate School of Systems Life Sciences, Kyushu University, Fukuoka, Japan
Applied Mathematics·Volume 03 (2012)·Pages 1463–1470·Published 31 October 2012·DOI10.4236/am.2012.330205
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Abstract
In Systems Biology, system identification, which infers regulatory network in genetic system and metabolic pathways using experimentally observed time-course data, is one of the hottest issues. The efficient numerical optimization algorithm to estimate more than 100 real-coded parameters should be developed for this purpose. New real-coded genetic algorithm (RCGA), the combination of AREX (adaptive real-coded ensemble crossover) with JGG (just generation gap), have applied to the inference of genetic interactions involving more than 100 parameters related to the interactions with using experimentally observed time-course data. Compared with conventional RCGA, the combination of UNDX (unimodal normal distribution crossover) with MGG (minimal generation gap), new algorithm has shown the superiority with improving early convergence in the first stage of search and suppressing evolutionary stagnation in the last stage of search.
KeywordsInverse ProblemS-System FormalismGene Regulatory NetworkSystem IdentificationReal-Coded Genetic Algorithm
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