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

Affiliation(s)

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.

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.

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.

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.

Cite this paper

A. Komori, Y. Maki, M. Nakatsui, I. Ono and M. Okamoto, "Efficient Numerical Optimization Algorithm Based on New Real-Coded Genetic Algorithm, AREX + JGG, and Application to the Inverse Problem in Systems Biology,"*Applied Mathematics*, Vol. 3 No. 10, 2012, pp. 1463-1470. doi: 10.4236/am.2012.330205.

A. Komori, Y. Maki, M. Nakatsui, I. Ono and M. Okamoto, "Efficient Numerical Optimization Algorithm Based on New Real-Coded Genetic Algorithm, AREX + JGG, and Application to the Inverse Problem in Systems Biology,"

References

[1] M. A. Savageau, “Biochemical Systems Analysis: A Study of Function and Design in Molecular Biology,” Addison-Wesley, Reading, Boston, 1976.

[2] D. Tominaga, N. Koga and M. Okamoto, “Efficient Numerical Optimization Algorithm Based on Genetic Algorithm for Inverse Problem,” Proceedings of the Genetic and Evolutionary Computation Conference, Las Vegas, 8-12 July 2000, p. 251.

[3] L. J. Eshleman and J. D. Schaffer, “Real-Coded Genetic Algorithms and Interval-Schemata,” Foundations of Genetic Algorithms, Vol. 2, 1993, pp. 187-202.

[4] I. Ono and S. Kobayashi, “A Real-Coded Genetic Algorithm for Function Optimization Using Unimodal Normal Distribution Crossover,” Journal of Japanese Society for Artificial Intelligence, Vol. 14, No. 6, 1999, pp. 1146-1155.

[5] I. Ono, S. Kobayashi and K. Yoshida, “Global and Multi-Objective Optimization for Lens Design by RealCoded Genetic Algorithms,” International Optical Design Conference Proceedings of SPIE, Vol. 3482, 1998, pp. 110-121.

[6] H. Sato, I. Ono and S. Kobayashi, “A New Generation Alternation Model of Genetic Algorithms and Its Assessment,” Journal of Japanese Society for Artificial Intelligence, Vol. 12, No. 5, 1997, pp. 734-744.

[7] H. Satoh, M. Yamamura and S. Kobayashi, “Minimal Generation Gap Model for GAs Considering Both Exploration and Exploitation,” Proceedings of 4th International Conference on Soft Computing, Iizuka, 30 September-5 October 1996, pp. 494-497.

[8] N. Shikata, Y. Maki, M. Nakatsui, M. Mori, Y. Noguchi, S. Yoshida, M. Takahashi, N. Kondo and M. Okamoto, “Determining Important Regulatory Relations of Amino Acids from Dynamic Network Analysis of Plasma Amino Acids,” Amino Acids, Vol. 38, No. 1, 2009, pp. 179-187. doi:10.1007/s00726-008-0226-3

[9] Y. Akimoto, R. Hasada, J. Sakuma, I. Ono and S. Kobayashi, “Generation Alternation Model for Real-Coded GA Using Multi-parent: Proposal and Evaluation of Just Generation Gap (JGG),” Proceedings of the 19th SICE Symposium on Decentralized Autonomous Systems, Tokyo, 29-30 January 2007, pp. 341-346.

[10] S. Kobayashi, “The Frontiers of Real-Coded Genetic Algorithms,” Journal of Japanese Society for Artificial Intelligence, Vol. 24, No. 1, 2009, pp. 147-162.

[11] Y. Akimoto, Y. Nagata, J. Sakuma, I. Ono and S. Kobayashi, “Proposal and Evaluation of Adaptive Real-Coded Crossover AREX,” Journal of Japanese Society for Artificial Intelligence, Vol. 24, No. 6, 2009, pp. 446-458.

[12] J. Sakuma and S. Kobayashi, “Latent Variable Crossover Fork-Tablet Structures and Its Application to Lens Design Problems,” Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2005), Washington DC, 2005, pp. 1347-1353.

[13] 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.

[14] T. Ueda, N. Koga, I. Ono and M. Okamoto, “EfficientNumerical Optimization Technique Based on Real-Coded Genetic Algorithmfor Inverse Problem,” Proceedings of 7th International Symposium on Artificial Life and Robotics, Beppu, Oita, 16-18 January 2002, pp. 290-293.

[15] T. Ueda, N. Koga, I. Ono and M. Okamoto, “Development of Efficient Numerical Optimization Method Based on Real-Coded Genetic Algorithm: Application to the Estimation of Large Number of Real-Valued Parameters,” Proceedings of the 7th International Symposium for Biochemical Systems Theory: From Phenotype to Genotype and Back, Averoy, More og Romsdal, 17-20 June 2002, pp. 43-44.

[16] M. Nakatsui, T. Ueda, Y. Maki, I. Ono and M. Okamoto, “Method for Inferring and Extracting Reliable Genetic Interactions from Time-Series Profile of Gene Expression,” Mathematical Biosciences, Vol. 215, No. 1, 2008, pp. 105-114. doi:10.1016/j.mbs.2008.06.007

[17] Y. Akimoto, J. Sakuma, I. Ono and S. Kobayashi, “Adaptation of Expansion Rate for Real-Coded Crossovers,” Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2009), Montreal, 8-12 July 2009, pp. 739-746.

[18] H. Kita, I. Ono and S. Kobayashi, “Multi-Parental Extension of the Unimodal Normal Distribution Crossover for Real-Coded Genetic Algorithms,” Proceedings of the IEEE Congress on Evolutionary Computation, Washington DC, 6-9 July 1999, pp. 1581-1587.

[1] M. A. Savageau, “Biochemical Systems Analysis: A Study of Function and Design in Molecular Biology,” Addison-Wesley, Reading, Boston, 1976.

[2] D. Tominaga, N. Koga and M. Okamoto, “Efficient Numerical Optimization Algorithm Based on Genetic Algorithm for Inverse Problem,” Proceedings of the Genetic and Evolutionary Computation Conference, Las Vegas, 8-12 July 2000, p. 251.

[3] L. J. Eshleman and J. D. Schaffer, “Real-Coded Genetic Algorithms and Interval-Schemata,” Foundations of Genetic Algorithms, Vol. 2, 1993, pp. 187-202.

[4] I. Ono and S. Kobayashi, “A Real-Coded Genetic Algorithm for Function Optimization Using Unimodal Normal Distribution Crossover,” Journal of Japanese Society for Artificial Intelligence, Vol. 14, No. 6, 1999, pp. 1146-1155.

[5] I. Ono, S. Kobayashi and K. Yoshida, “Global and Multi-Objective Optimization for Lens Design by RealCoded Genetic Algorithms,” International Optical Design Conference Proceedings of SPIE, Vol. 3482, 1998, pp. 110-121.

[6] H. Sato, I. Ono and S. Kobayashi, “A New Generation Alternation Model of Genetic Algorithms and Its Assessment,” Journal of Japanese Society for Artificial Intelligence, Vol. 12, No. 5, 1997, pp. 734-744.

[7] H. Satoh, M. Yamamura and S. Kobayashi, “Minimal Generation Gap Model for GAs Considering Both Exploration and Exploitation,” Proceedings of 4th International Conference on Soft Computing, Iizuka, 30 September-5 October 1996, pp. 494-497.

[8] N. Shikata, Y. Maki, M. Nakatsui, M. Mori, Y. Noguchi, S. Yoshida, M. Takahashi, N. Kondo and M. Okamoto, “Determining Important Regulatory Relations of Amino Acids from Dynamic Network Analysis of Plasma Amino Acids,” Amino Acids, Vol. 38, No. 1, 2009, pp. 179-187. doi:10.1007/s00726-008-0226-3

[9] Y. Akimoto, R. Hasada, J. Sakuma, I. Ono and S. Kobayashi, “Generation Alternation Model for Real-Coded GA Using Multi-parent: Proposal and Evaluation of Just Generation Gap (JGG),” Proceedings of the 19th SICE Symposium on Decentralized Autonomous Systems, Tokyo, 29-30 January 2007, pp. 341-346.

[10] S. Kobayashi, “The Frontiers of Real-Coded Genetic Algorithms,” Journal of Japanese Society for Artificial Intelligence, Vol. 24, No. 1, 2009, pp. 147-162.

[11] Y. Akimoto, Y. Nagata, J. Sakuma, I. Ono and S. Kobayashi, “Proposal and Evaluation of Adaptive Real-Coded Crossover AREX,” Journal of Japanese Society for Artificial Intelligence, Vol. 24, No. 6, 2009, pp. 446-458.

[12] J. Sakuma and S. Kobayashi, “Latent Variable Crossover Fork-Tablet Structures and Its Application to Lens Design Problems,” Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2005), Washington DC, 2005, pp. 1347-1353.

[13] 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.

[14] T. Ueda, N. Koga, I. Ono and M. Okamoto, “EfficientNumerical Optimization Technique Based on Real-Coded Genetic Algorithmfor Inverse Problem,” Proceedings of 7th International Symposium on Artificial Life and Robotics, Beppu, Oita, 16-18 January 2002, pp. 290-293.

[15] T. Ueda, N. Koga, I. Ono and M. Okamoto, “Development of Efficient Numerical Optimization Method Based on Real-Coded Genetic Algorithm: Application to the Estimation of Large Number of Real-Valued Parameters,” Proceedings of the 7th International Symposium for Biochemical Systems Theory: From Phenotype to Genotype and Back, Averoy, More og Romsdal, 17-20 June 2002, pp. 43-44.

[16] M. Nakatsui, T. Ueda, Y. Maki, I. Ono and M. Okamoto, “Method for Inferring and Extracting Reliable Genetic Interactions from Time-Series Profile of Gene Expression,” Mathematical Biosciences, Vol. 215, No. 1, 2008, pp. 105-114. doi:10.1016/j.mbs.2008.06.007

[17] Y. Akimoto, J. Sakuma, I. Ono and S. Kobayashi, “Adaptation of Expansion Rate for Real-Coded Crossovers,” Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2009), Montreal, 8-12 July 2009, pp. 739-746.

[18] H. Kita, I. Ono and S. Kobayashi, “Multi-Parental Extension of the Unimodal Normal Distribution Crossover for Real-Coded Genetic Algorithms,” Proceedings of the IEEE Congress on Evolutionary Computation, Washington DC, 6-9 July 1999, pp. 1581-1587.