Ryoei Takahashi

Ryoei Takahashi

Profile

  • Bachelor of Science, Master of Science, Doctor of Philosophy, Waseda University
  • Former Professor of Systems and Information Engineering, Graduate School of Technology, Hachinohe Institute of Technology, Former employee of NTT Yokosuka Telecommunications Research Institute, Former employee of NTT Information Sharing Platform Laboratories
 

Message

Future vision of the IT sector

"Developing Data Analysts"

At an enterprise research center, I have experienced R & D and practical application of communication software (middle software package between the OS and the AP) in the development of a large computer (DIPS: Domestic machine to compete with IBM SYSTEM/370). In applying this to practical systems such as financial information distribution systems, it has become important to analyze the causes of problems and eliminate them and defects in software, which can cause huge losses to system users.

In recent years, use of computer networks have spread to various industrial fields, and their information management systems maintain huge software assets. In software maintenance, in order to determine the functionality, test items, and test procedure of the modified software, we need to understand the software design documents, source programs written in C language, and manuals for the original software that is to be reused. As a result, complexity of software that is reused has a significant impact on software maintenance costs and maintenance quality. The complexity of this software is a major determinant of software quality, as noted by Boehm, an expert in Software Engineering Economics.

Against this background, the industry is expected to develop human resources who can not only develop high-quality software based on an object-oriented approach, but also accumulate productivity (Quality) data on the number of failures and their causes, etc., during software system development, and analyze the data using various statistical methods, and reflect the analysis results in the next system development.

In addition, as mentioned above, when the objective function of a system is complex, such as in the analysis of the causes of software defects, it is necessary to learn how to search for the optimal model using stochastic methods such as machine learning (supervised learning and unsupervised learning), genetic algorithms, which are optimization methods that mimic the evolution of life, ant colony optimization, which is an optimization method that mimics the foraging behavior of ants, and neural networks, which are optimization methods that mimic human neural networks.

Responsible Subject

  • Machine Learning
  • Project basic exercise

Field of Specialization

  • Informatics (Soft Computing), Software Engineering

Business Performance

Paper

  • Ryoei Takahashi, Empirical Evaluation of Changing Crossover Operators to Solve Function Optimization Problems, Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016), pp. 1 -10, DOI: 10.1109/SSCI. 2016.7850141, 2016
  • Ryoei Takahashi, Verification of Thermo-dynamic Genetic Algorithm to Solve the Function Optimization Problem through Diversity Measurement, Proceedings of the 2016 IEEE Congress on Evolutionary Computation (IEEE CEC 2016), pp. 168 -177, DOI 10.1109/CEC. 2016.7743792, 2016
  • Ryoei Takahashi, Quantitative Evaluation of Iterative Extended Changing Crossover Operators to Solve the Traveling Salesman Problem, Proceedings of 2014 10 th International Conference on Natural Computation (ICNC), pp. 235 -244, IEEE, 2014
  • Ryoei Takahashi, Solving the Traveling Salesman Problem through Iterative Extended Changing Crossover Operators, Proceedings of Tenth International Conference on Machine Learning and Applications, pp. 253 -258, IEEE Man, Machine and Cybernetics Society, 2011
  • Ryoei Takahashi, Extended Changing Crossover Operators to Solve the Traveling Salesman Problem, Electronics and Communications in Japan, Vol. 93, NO. 7, pp 1 -16, 2010
  • Ryoei Takahashi, A Hybrid Method of Genetic Algorithms and Ant Colony Optimization to Solve the Traveling Salesman Problem, Proceedings of Eighth International Conference on Machine Learning and Applications, pp. 81 -88, IEEE Man, Machine and Cybernetics Society, 2009
  • Solution to the traveling salesman problem by the extended crossover operator method, Institute of Electrical Engineers, Vol. 128, No. 12, Sec. C., pp. 1820 -1832, 2008
  • Ryoei Takahashi, A Methodology of Extended Changing Crossover Operators to Solve the Traveling Salesman Problem, The 4th International Conference on Natural Computation (ICNC 08) and The 5th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD '08), pp. 263 -269, IEEE, 2008
  • Ryoei Takahashi, A Performance Improvement of Solving the Traveling Salesman Problem through Uniting Changing Crossover Operators to Ant Colony Optimization, "Advance in Natural Computation and Data Mining, Proceedings the 2nd International Conference on Natural Computation and the 3rd International Conference on Fuzzy Systems and Knowledge Discovery, pp. 114 -130, Xi ’ an University, 2006
  • Ryoei Takahashi and Kenichi Degai, A Performance Improvement of Genetic Algorithms through Changing Crossover Operators to Solve the Traveling Salesman Problem, Proceedings of the 8th International Conference on Computer and Information Technology, pp. 40 -45, organized by Islamic University of Technology, 2005
  • Ryoei Takahashi, Solving the Traveling Salesman Problem through Genetic Algorithms with Changing Crossover Operators, Proceedings of Fourth International Conference on Machine Learning and Applications, p. 319 -324, IEEE Computer Society, 2005
  • Ryoei Takahashi, Solving the Traveling Salesman Problem through Changing Crossover Operators, Proceedings of the 10 th Conference on Artificial Intelligence and Applications, p. 42, hosted by National University of Kaohsiung, 2005
  • Ryouei Takahashi, Yukihiro Nakamura, Yoichi Muraoka, Satoru Ikehara, Empirical Validation of Project-Independence of Quantitative Relationship between Program Fault Density and Complexity Metrics, The Seventh IEEE Workshop on Empirical Studies of Software Maintenance, WESS 2001, pp. 101 -106, Florence, Italy, IEEE Computer Society, 2001
  • Ryoei Takahashi, Verification of Complexity Metrics for Evaluating Software Quality, Journal of the Institute of Electronics, Information and Communication Engineers (D-I), Vol. J 84 D-I, No. 7, pp. 1030 -1044, 2001.
  • Ryoei Takahashi, Analysis of the Relationship between Halstead's Software Science Measurement and Fault Density in the C Software Maintenance Process, Journal of the Institute of Electronics, Information and Communication Engineers (D-I), Vol. J 82 D-I, No. 8, pp. 1017 -1034, 1999
  • Ryoei Takahashi, Complexity Measured Using Halstead's Software Science in the Software Maintenance Phase and Analysis of Its Impact on Fault Density, Proc. 4th Int. Conf. Achieving Quality in Software, AQUIS '98, pp. 185 -194, Venice, Italy, IEI-CNR and QUALITAL, 1998
  • Ryoei Takahashi, Yoichi Muraoka, Hiroshi Nakamura, How to Generate and Evaluate Software Quality Classification Trees, Journal of the Japan Society of Electronics, Information and Communication Engineers (D-I), Vol. 81 - D I, No. 4, pp. 393, -404, 1998
  • Ryoei Takahashi, Yoichi Muraoka, Yukihiro Nakamura, Building Software Quality Classification Trees: Approach, Experiment, Evaluation, Proc. Eighth Int. Symp. Software Reliability Engineering, ISSRE '97, pp. 222 -233, Albuquerque, New Mexico, IEEE Computer Society, 1997
  • Ryouei Takahashi, Software Quality Classification Model Based on McCabe's Complexity Measure, J. Systems and Software, vol.38, pp.61-69, 1997
  • Ryoei Takahashi, Yukihiro Nakamura, Evaluation of Software Maintenance Quality Based on Interface`s Complexity between Divisions and Modification, Journal of the Japan Society of Electronics, Information and Communication Engineers (D-I), Vol. 80, D-I, No. 5, pp. 441 -449, 1997
  • Ryoei Takahashi, Yukihiro Nakamura, The Effect of Interface Complexity on Program Error Density, Proc. Int. Conf. Software Maintenance, ICSM '96, pp. 77 -86, Money, California, IEEE Computer Society, 1996.11
  • Ryoei Takahashi, Software Quality Assessment Model Based on the Complexity of Relations Between Yoshihide and Mojul: Examples by Nesting, Institute of Electronics, Information and Communication Engineers (D-I), Vol. J 79, D-I, No. 5, pp. 261, -270, 1996.5
  • Ryoei Takahashi, Software Quality Classification Model Based on McCabe's Complexity Measure, Proc. Third Int. Conf. Achieving Quality in Software, AQUIS ’ 96, pp. 189 -200, Florence, Italy, IFIP * (International Federation for Information Processing), 1996
  • Ryoei Takahashi, Hirofumi Wakayama, Discriminative Efficiency Methodology for Validating Software Quality Classification Models, Systems and Computers in Japan, vol. 26, no. 5, pp. 1 -18, 1995
  • Ryoei Takahashi, Hirofumi Wakayama, Evaluation Method of Software Quality Discriminant Model by Discriminant Efficiency, Journal of the Institute of Electronics, Information and Communication Engineers (D-I), vol. J 77 - D-I, no. 10, pp. 663 -673, 1994
  • Hirofumi Wakayama, Ryoei Takahashi, A Software Development Cost Estimation Method Based on Software Classification, Proc. 3rd Int. Conf. Software Quality, 3 ICSQ, pp. 207 -214, the Hyatt Lake Tahoe, Nevada, ASQC (American Society of Quality Control), 1993.10
  • Ryoei Takahashi, Hirofumi Wakayama, Construction of Software Cost Estimation Model by AIC Considering Influence from the Society of Electronics, Information and Communication Engineers (D-I), Vol. 76 - D-I, No. 2, pp. 72 -79, 1993
  • Ryoei Takahashi, A Methodology of Generating the Optimum Software Quality Classification Tree by Using Genetic Algorithms, WESS 2004, Ninth IEEE Workshop on Empirical Studies of Software Maintenance, Sep -17, 2004, Chicago, Illinois, 2004

Grant-in-Aid for Scientific Research

  • Fundamental Research (C) (General) Task Number: 15 K00347, Assistance Period: FY 2015 to FY 2019, Research Section Title [Research on the measurement of population diversity in genetic algorithm and its application to selection strategy]