Kyushu University Academic Staff Educational and Research Activities Database
List of Presentations
Ryohei Funaki Last modified date:2018.09.13

Assistant Professor / Measurement and Control Engineering / Department of Electrical Engineering / Faculty of Information Science and Electrical Engineering

1. Masaru Murakami, Ryohei Funaki, Junichi Murata, Design of Incentive-Based Demand Response Programs Using Inverse Optimization, IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2017, 2017.10.
2. Junichi Murata, Masaru Murakami, Ryohei Funaki, Energy Management Systems and their Element Design Example: a General Overview and a Demand Response Program Design, The 3rd International Conference on Universal Village (UV2016), 2016.10.
3. Edmund Soji Otabe, Takuto Taguchi, Yuuki Tsuruda, Ryohei Funaki, Determining Pinning Parameters in Flux Creep-Flow Model for E-J characteristics of High Temperature Superconductors by using Differential Evolution, The 29th International Superconductivity Symposium (ISS2016), 2016.12.
4. Ryohei Funaki, Hirotaka Takano, Junichi Murata, Tree Structure Based Differential Evolution for Optimization of Trees and Interactive Evolutionary Computation, Proceedings of the 34th Chinese Control Conference and SICE Annual Conference 2015, 2015.07, [URL].
5. Ryohei Funaki, Hirotaka Takano, Junichi Murata, Re-labeling Differential Evolution for Combinatorial Optimization, Proceedings of SICE Annual Conference 2013, 2013.09.
6. Ryohei Funaki, Hideyuki Takagi, Application of gravity vectors and moving vectors for the acceleration of both differential evolution and interactive differential evolution, Proceedings of 5th Int. Conf. on Genetic and Evolutionary Computing (ICGEC2011), 2011.08, We propose and evaluate two methods for accelerating differential evolution and interactive differential evolution (IDE). The first acceleration method, which we call DE/gravity, aims to realize performance similar to that of paired-comparison-based IDE/best while removing the requirement that the IDE user must choose the best individual among all displayed individuals. The second acceleration method generates not only a conventional trial vector but also a second and third trial vector. It calculates a moving average vector, X moving, for the population between generations, and compares a given target vector with the three trial vectors of a conventional trial vector, a target vector + X_moving, and a trial vector + X_moving, and uses the best one among the four vectors as offspring in the next generation. We evaluate these acceleration methods and a conventional method by applying them to Gaussian mixture models and demonstrate the effectiveness of our proposed methods..