Abstract.
We propose an optimization algorith based on parallelized genetic algorithm (GA) for inference biological scale-free network. The optimization task is to infer a structure of biochemical network only from time-series data of each biochemical element. This is a inverse problem which can not be solved analytically, and only heulistic searches such as GA, simulated annealing, etc. are practically effective. We applied our algorithm for several case studies to prove its effectiveness. The results shows high parallelization efficiency of our GA based algorithm and importance of scale-free property.
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© 2003 Springer-Verlag Berlin Heidelberg
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Tominaga, D., Takahashi, K., Akiyama, Y. (2003). Distributed Genetic Algorithm for Inference of Biological Scale-Free Network Structure. In: Veidenbaum, A., Joe, K., Amano, H., Aiso, H. (eds) High Performance Computing. ISHPC 2003. Lecture Notes in Computer Science, vol 2858. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39707-6_16
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DOI: https://doi.org/10.1007/978-3-540-39707-6_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20359-9
Online ISBN: 978-3-540-39707-6
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