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I. Ono, H. Kita and S. Kobayashi, “A Robust Real-Coded Genetic Algorithm Using Unimodal Normal Distribution Crossover Augmented by Uniform Crossover: Effects of Self-Adaptation of Crossover Probabilities,” Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-1999), Orlando, 1999, pp. 496-503.

has been cited by the following article:

• TITLE: Efficient Numerical Optimization Algorithm Based on New Real-Coded Genetic Algorithm, AREX + JGG, and Application to the Inverse Problem in Systems Biology

  AUTHORS: Asako Komori, Yukihiro Maki, Masahiko Nakatsui, Isao Ono, Masahiro Okamoto

  KEYWORDS: Inverse Problem; S-System Formalism; Gene Regulatory Network; System Identification; Real-Coded Genetic Algorithm

  JOURNAL NAME: Applied Mathematics, Vol.3 No.10A, November 1, 2012

  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.