■Faculty | Department of Computational Systems Biology / Graduate School of Biology-Oriented Science and Technology |

■Position | Lecturer |

■Degree | |

■Commentator Guide | https://www.kindai.ac.jp/meikan/901-koumoto-keiko.html |

■URL | |

Last Updated :2020/11/25

- - 2003 , Okayama University of Science, Graduate School, Division of Engineering

- 2004 04 , - 2005 03 , School of Engineering, Department of Media Technologies, Nippon Bunri University
- 2003 , - Hiroshima National College of Maritime Technology,
- Research assistant
- Distribution and Information Engineering Department,

- Informatics, Sensitivity (kansei) informatics
- Informatics, Soft computing
- Informatics, Intelligent informatics

- Evolutionary Computation, Evolutionary Programming, combinatorial optimization problem, Local Search, Genetic Algorithm, Genetic Local Search, Binary Quadratic Programming Problem

- Transmittancy of Melanin Pigment Using Optical Spectrum Analyzer, KOHMOTO Keiko, KUWASHIMA Fumiyoshi, Proc. of the 2019 International Symposium on Nonlinear Theory and Its Applications (NOLTA2019), Proc. of the 2019 International Symposium on Nonlinear Theory and Its Applications (NOLTA2019), Dec. 2019 , Refereed
- Transmittancy of melanin pigment using laser irradiations, KOHMOTO Keiko, KUWASHIMA Fumiyoshi, Proc. of the 2017 International Symposium on Nonlinear Theory and Its Applications (NOLTA2018), Proc. of the 2017 International Symposium on Nonlinear Theory and Its Applications (NOLTA2018), Sep. 2018 , Refereed
- Optimal infrared wave length for healing pigmentations, Keiko Kohmoto, Fumiyoshi Kuwashima, Dec. 2017 , Refereed
- Numerical simulation of gene expression pattern in plant leaf, T. Ichino, K. Kohmoto, H. Fukuda, Mar. 2013 , Refereed
- Spiral Waves in Gene Expression of Cellular Circadian Clocks in Plant Leaf, KOHMOTO Keiko, 16, 191 - 196, Oct. 2012 , Refereed
- A dependence of wavelength on a temperature rise in a black pigment, Proc. of 22nd SICE Symposium on Biological and Physiological Engineering, Proc. of 22nd SICE Symposium on Biological and Physiological Engineering, 103 - 106, Jan. 2008 , Refereed
- Performance of Exponential Evolutionary Programming, H. NARIHISA, K. KOHMOTO, T. KUMON, K. KATAYAMA, Proc. of the 7th IASTED International Conference on Artificial Intelligence and Soft Computing (ASC2003), Proc. of the 7th IASTED International Conference on Artificial Intelligence and Soft Computing (ASC2003), pp. 243-248, Jul. 2003 , Refereed
- Performance of Evolutionary Programming using Exponential Mutation, K. KOHMOTO, K. KATAYAMA, H. NARIHISA, Proc. of the 4th Asia-Pacific Conference on Simulated Evolution And Learning (SEAL2002) VOLUME 2, Proc. of the 4th Asia-Pacific Conference on Simulated Evolution And Learning (SEAL2002) VOLUME 2, pp. 454--458, 454 - 458, Nov. 2002 , Refereed
- Evolutionary Programming with Double Exponential Probability Distribution, K. KOHMOTO, K. KATAYAMA, H. NARIHISA, Proc. of the second International Association of Science and Technology for Development (IASTED) International Conference on Artificial Intelligence and Applications (AIA2002), Proc. of the second International Association of Science and Technology for Development (IASTED) International Conference on Artificial Intelligence and Applications (AIA2002), pp. 358--363, 358 - 363, Sep. 2002 , Refereed
- Empirical Knowledge of a Parameter Setting in k-opt Local Search for the Binary Quadratic Programming Problem, K. KOHMOTO, K. KATAYAMA, H. NARIHISA, Proc. of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01) Workshop on Empirical Methods in Artificial Intelligence, Proc. of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01) Workshop on Empirical Methods in Artificial Intelligence, 21 - 26, Aug. 2001 , Refereed
- Performance of a Genetic Algorithm for the Graph Partitioning Problem, K. KOHMOTO, K. KATAYAMA, H. NARIHISA, Proc. The First Western Pacific/Third Australia-Japan Workshop on Stochastic Models, Proc. The First Western Pacific/Third Australia-Japan Workshop on Stochastic Models, 315 - 324, Sep. 1999 , Refereed
- Memetic algorithm with strategic controller for the maximum clique problem, Kengo Katayama, Akinori Kohmura, Keiko Kohmoto, Hideo Minamihara, Proceedings of the ACM Symposium on Applied Computing, Proceedings of the ACM Symposium on Applied Computing, 2, 1062 - 1069, 2011 , RefereedSummary:Most of standard evolutionary algorithms consist of a mutation, a crossover, a selection and often a local search. Each of these operators is specifically designed for a combinatorial optimization problem. These can be considered as tools for the optimization searches, and the interplay between them in the searches is not apparently controlled in many cases. In this paper, we present a flexible control method, called Strategic Controller (SC), for multiple mutation methods equipped in a memetic algorithm (MA) for the maximum clique problem (MCP). The SC is used to choose a suitable method from the candidate mutations. To perform an adaptive search, the SC evaluates each mutation method based on the contribution information which is served as novel "memes" for the mutations in the MA. To achieve the SC, we apply the idea of analytic hierarchy process. Although standard MAs have a population of multiple solutions as memes usually, a single solution is used in our MA. We evaluated the performance of MA with SC (MA-SC) on DIMACS benchmark graphs of the MCP. The results showed that MA-SC is capable of finding comprehensive solutions through comparisons with MAs in which each mutation is used. Moreover, we observed that it is highly effective particularly for hardest graphs in the benchmark set in comparisons with recent metaheuristics to the MCP. © 2011 ACM.
- Evolutionary programming with only using exponential mutation, H. Narihisa, K. Kohmoto, T. Taniguchi, M. Ohta, K. Katayama, 2006 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-6, 2006 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-6, 552 - +, 2006 , RefereedSummary:The individual of population in standard self-adaptive evolutionary programming (EP) is composed as a pair of objective variable and strategy parameter. Therefore, EP must evolve both objective variable and strategy parameter. In standard evolutionary programming (CEP), these evolutions are implemented by mutation based on only Gaussian random number. On the other hand, fast evolutionary programming (FEP) uses Cauchy random number as evolution of objective variable and exponential evolutionary programming (EEP) uses exponential random number as evolution of objective variable. However, all of these EP (CEP, FEP and EEP) commonly uses Gaussian random number as evolution of strategy parameter. In this paper, we propose new EEP algorithm (NEP) which uses double exponential random number for both evolution of objective variable and strategy parameter. The experimental results show that this new algorithm (NEP) outperforms the existing CEP and FEP.
- Convergence characteristics of exponential evolutionary programming, H Narihisa, K Kohmoto, Proceedings of the Eighth IASTED International Conference on Artificial Intelligence and Soft Computing, Proceedings of the Eighth IASTED International Conference on Artificial Intelligence and Soft Computing, 426 - 431, 2004 , RefereedSummary:In this paper, we present the convergence characteristics of exponential evolutionary programming (EEP) which uses a mutation based on double exponential probability distribution with positive parameter value. Concerning the distribution of random number which is used in mutation of evolutionary pro-ramming (EP), it is desirable that the variance of the distribution should be large in first stage of evolution and should be small in mid to final stage of evolution applying on optimization problems. In order to realize such distribution of random number, we propose EEP with multi switching of parameter value of the distribution from initial value to next value at some arbitrary generation so as to decrease the variance of the distribution. Experimental results show that these switching EEP can improve convergence performance of EEP and switching effect can be recognized.
- Performance of a genetic algorithm for the graph partitioning problem, K Kohmoto, K Katayama, H Narihisa, MATHEMATICAL AND COMPUTER MODELLING, MATHEMATICAL AND COMPUTER MODELLING, 38(11-13), 1325 - 1332, Dec. 2003 , RefereedSummary:The performance of the genetic algorithm (GA) for the graph partitioning problem (GPP) is investigated by comparison with standard heuristics on well-known benchmark graphs. In general, there is a case where a practical performance of a conventional genetic approach, which performs only simple operations without a local search strategy, is not sufficient. However, it is known that a combination of GA and local search can produce better solutions. From this practice, we incorporate a simple local search algorithm into the GA. In particular, the search ability of the GA is compared with standard heuristics such as multistart local search and simulated annealing, which use the same neighborhood structure of the simple local search, for solving the GPP. Experimental results show that the GA performs better than its competitors. (C) 2003 Elsevier Ltd. All rights reserved.
- Evolutionary programming using exponential mutation, K Kohmoto, H Narihisa, K Katayama, 6TH WORLD MULTICONFERENCE ON SYSTEMICS, CYBERNETICS AND INFORMATICS, VOL XI, PROCEEDINGS, 6TH WORLD MULTICONFERENCE ON SYSTEMICS, CYBERNETICS AND INFORMATICS, VOL XI, PROCEEDINGS, vol. XI, Computer Science Ⅱ, pp. 405-410, 405 - 410, 2002 , RefereedSummary:In this paper, we present an efficient evolutionary programming with the mutation operator based on double exponential probability distribution concerning the mutation operator of evolutionary programming. There have been proposed various mutation operators by many researchers. However, the mutation operator is mainly based on normal probability distribution or Cauchy probability distribution to evolve solution for given optimization problems. The double exponential probability distribution with one positive real valued parameter has some positive amount variance and is symmetric with respect to origin. Although the variance of this probability distribution is neither infinite as Cauchy distribution, nor unit as standardized normal distribution, the amount of this variance is controllable by the value of this parameter. This fact plays an important role at the evolution process in evolutionary programming. The results of computational experiment show that our proposed evolutionary programming with double exponential probability distribution performs much better than the conventional evolutional programming when applied to the optimization problems which are well known as the benchmark problems in this research field.

- Transmittancy of Melanin Pigment Using Optical Spectrum Analyzer, Keiko Kohmoto, Fumiyoshi Kuwashima, The 2019 International Symposium on Nonlinear Theory and Its Applications, 2019 12
- Transmittancy of melanin pigment using laser irradiations, Keiko Kohmoto, Fumiyoshi Kuwashima, The 2018 International Symposium on Nonlinear Theory and Its Applications, 2018 09
- Optimal infrared wave length for healing pigmentations, Keiko Kohmoto, Fumiyoshi Kuwashima, The 2017 International Symposium on Nonlinear Theory and Its Applications, 2017 12

- Genetic Local Search for the Binary Quadratic Programming Problem