Kyushu University Academic Staff Educational and Research Activities Database
List of Presentations
Hideyuki Takagi Last modified date:2019.09.05

Professor / Modeling and Optimization / Department of Human Science / Faculty of Design

1. Yuhao Li, Jun Yu, Hideyuki Takagi, Ying Tan, Accelerating Fireworks Algorithm with Weight-based Guiding Sparks, 2019 IEEE Congress on Evolutionary Computation (CEC2019), 2019.07, [URL].
2. Jun Yu, Hideyuki Takagi, Ying Tan, Fireworks Algorithm for Multimodal Optimization Using a Distance-based Exclusive Strategy, 2019 IEEE Congress on Evolutionary Computation (CEC2019), 2019.06, [URL], We propose a distance-based exclusive strategy to extend fireworks algorithm as a niche method to find out multiple global/local optima. This strategy forms sub-groups consisting of a firework individual and its generated spark individuals, each sub-group is guaranteed not to search overlapped areas each other. Finally, firework individuals are expected to find different global/local optima. The proposed strategy checks the distances between a firework and other fireworks which fitness is better than that of the firework. If the distance between two firework individuals is shorter than the sum of their searching radius, i.e. amplitude of firework explosions, these two firework individuals are considered to search overlapped area. Thus, the poor firework is removed and replaced by its opposite point to track multiple optima. To evaluate the performance of our proposed strategy, enhanced fireworks algorithm (EFWA) is used as a baseline algorithm and combined with our proposal. We design a controlled experiment, and run EFWA and (EFWA + our proposal) on 8 benchmark functions from CEC 2015 test suite, that is dedicated to single objective multi-niche optimization. The experimental results confirmed that the proposed strategy can find multiple different optima in one trial run..
3. Niche Fireworks Algorithm by Distance-based Exclusive Strategy.
4. Jun Yu and Hideyuki Takagi, "Performance Analysis of Vegetation Evolution," Evolutionary Computation Symposium 2018, Fukuoka, Japan, pp.27-21 (December 8-9, 2018). (in Japanese)..
5. 余俊, 裴岩,髙木英行, Competitive Strategies for Differential Evolution, 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC2018), 2018.10, We introduce two competitive strategies into conventional differential evolution (DE) to speed up its convergence by increasing competitive pressures among individuals and evaluate the proposals. The first strategy gives individuals with better fitness a higher opportunity for generating more offsprings, while conventional DE allows each parent individual to generate only one offspring individual fairly. This strategy compares each of poor individuals with a randomly selected individual from the current population. If the latter becomes a winner, the latter can generate one more offspring individual, but the former loses an opportunity for generating its offspring. If the former becomes a winner, no one loses this opportunity, and each of them generates one offspring individual. The second strategy does not compare a generated offspring individual with its parent but the worst individual in the current population, which can accelerate the elimination of poor individuals and keep better individuals. We design a set of controlled experiments to evaluate these two strategies using CEC2013 benchmark functions with three different dimensions. The experimental results indicate that properly enhancing competition among individuals in DE can speed up its convergence and improve optimization performance..
6. Hideyuki Takagi, Keisuke Ikeda and Weiqiang Lai, Human Awareness Support by Changing Values of Hidden Factors of Input Stimuli Dynamically, 9th IEEE International Conference on Awareness Science and Technology (iCAST 2018), 2018.09, We propose an awareness support system that helps a user to be aware of the reason of his/her evaluations. Based on our proposed definition of human awareness mechanism, we extract hidden factors of input information using an auto-encoder neural networks and implement its decoder part into an awareness support system. The big feature of this system is to let a user change the values of the extracted hidden factors manually and observe the system outputs that change according to the changes of hidden factors. Experimental results using a task of generating facial emotions with 21 human subjects have shown the effectiveness of this approach..
7. 余俊, 髙木英行, Vegetation Evolution for Numerical Optimization, JPNSEC International Workshop on Evolutionary Computation, 2018.09, We propose a new population-based evolutionary algorithm (EA) by simulating vegetation growth and reproduction repeatedly to find a global optimal solution. In nature, many plants use various survival mechanism to guarantee thrive and their seeds disperse everywhere, then generated seeds root in a new suitable environment then repeat growth experience of their father. Inspired by this process, we develop a new optimization framework, where an individual consists of two periods: growth and maturity. In the growth period, each individual focus on exploitation through competing survival resources to achieve its better growth, while all individuals move to exploration by intraspecific cooperation to achieve the population continued. Therefore, the new proposal alternately performs two periods to balance exploitation and exploration. To evaluate the performance of our proposed algorithm, we compare it with the other three widely known algorithms in EA community, differential evolution (DE), particle swarm optimization (PSO) and enhanced fireworks algorithm (EFWA), and apply them to 28 benchmark functions from CEC2013 test suites of 2-dimensions (2-D), 10-D and 30-D with 30 trial runs each. The experimental results confirm that our proposal is effective and potential. Finally, we analyze the effects of the composition of our proposal on performance, and some open topics are given..
8. 余俊, 譚営,髙木英行, Scouting Strategy for Biasing Fireworks Algorithm Search to Promising Directions, Genetic and Evolutionary Computation Conference (GECCO2018), 2018.07, We propose a scouting strategy to find better searching directions in fireworks algorithm (FWA) to enhance its exploitation capability. It generates spark individuals from a firework individual one by one by checking if the generated spark climbs up to a better direction, and this process continues until spark individual climbing down is generated, while canonical FWA generates spark individuals around a firework individual at once. We can know potential search directions from the number of consciously climbing up sparks. Besides this strategy, we use a filtering strategy for a random selection of FWA, where worse sparks are eliminated when their fitness is worse than their parents, i.e. fireworks, and become unable to survive in the next generation. We combined these strategies with the enhanced FWA (EFWA) and evaluated using 28 CEC2013 benchmark functions. Experimental results confirm that the proposed strategies are effective and show better performance in terms of convergence speed and accuracy. Finally, we analyze their applicability and provide some open topics..
9. 余俊, 譚営,髙木英行, Accelerating Fireworks Algorithm with an Estimated Convergence Point, 9th Int. Conf. on Swarm Intelligence (ICSI’2018), 2018.06, We propose an acceleration method for the fireworks algorithms which uses a convergence point for the population estimated from moving vectors between parent individuals and their sparks. To improve the accuracy of the estimated convergence point, we propose a new type of firework, the {\em synthetic firework}, to obtain the correct of the local/global optimum in its local area’s fitness landscape. The synthetic firework is calculated by the weighting moving vectors between a firework and each of its sparks and replacing the worst firework individual in the next generation. We design a controlled experiment for evaluating the proposed strategy and apply it to 20 CEC2013 benchmark functions of 2-dimensions (2-D), 10-D and 30-D with 30 trial runs each. The experimental results and the Wilcoxon signed-rank test confirm that the proposed method can significantly improve the performance of the canonical firework algorithm..
10. Scouting Strategy Applied to Fireworks Algorithm.
11. Estimation of Convergence Points of Population Using an Individual Pool,.
12. Improving Estimation Precision of the Convergence Point of Individuals Using Weight-based Moving Vectors,.
13. Accelerating Interactive Evolutionary Computation Using an Estimated
Convergence Point.
14. Acceptability for Many-Objective Searching of Rental Apartments.
15. Yu Jun, Yan Pei, Hideyuki Takagi, Accelerating Evolutionary Computation Using Estimated Convergence Point, IEEE Congress on Evolutionary Computation (CEC2016), 2016.07.
16. Jun Yu and Hideyuki Takagi, "Two-stage Explosions for Optimization Based on Fireworks Algorithm," 10th Evolutionary Computing Meeting, pp.19-23, Kawasaki, Japan (March 17-18, 2016) (in Japanese)..
17. Yan Pei, Jun Yu and Hideyuki Takagi, "Evaluation of EC Acceleration by Using Estimated Points," Evolutionary Computation Symposium 2015, pp.292-297, Nishio, Japan, 297 (December 19-20, 2015). (in Japanese)..
18. Makoto Inoue and Hideyuki Takagi, "Introducing Selection Probability for EMO-based Architectural Room Floor Planning,"SICE System and Information Meeting (SSI2015), SS5-8, pp.734-737, Hakodake, Hokkaido (November 18-20, 2015) (in Japanese)..
19. Jun Yu, Hideyuki Takagi, Clustering of Moving Vectors for Evolutionary Computation, 7th Int. Conf. on Soft Computing and Pattern Recognition (SoCPaR2015), 2015.11.
20. Hideyuki Takagi, Makoto Inoue and Yan Pei, "Introduction of Acceptability to evolutionary multi-objective optimization," 9th Evolutionary Computing Meeting, pp.18-23, Kobe, Japan (Sept. 7-9, 2015) (in Japanese)..
21. Jun Yu and Hideyuki Takagi, "Correction methods for improving estimated convergence points for multi-modal optimization," 9th Evolutionary Computing Meeting, pp.92-97, Kobe, Japan (Sept. 7-9, 2015) (in Japanese)..
22. Yan Pei, Hideyuki Takagi, Local Information of Fitness Landscape Obtained by Paired Comparison-Based Memetic Search for Interactive Differential Evolution, IEEE Congress on Evolutionary Computation (CEC2015), 2015.05.
23. Noboru Murata, Ryuei Nishii, Hideyuki Takagi, Yan Pei, Analytical Estimation of the Convergence Point of Populations, IEEE Congress on Evolutionary Computation (CEC2015), 2015.05, [URL].
24. Noboru Murata, Ryuei Nishii, Hideyuki Takagi, and Yan Pei, "Estimation Methods of the Convergence Point of Moving Vectors Between Generations," Japanese Society for Evolutionary Computation Symposium 2014, Hatsukaichi, Japan (20-21, Dec., 2014). (in Japanese).
25. Makoto Inoue, Megumu Hiramoto, Muneyuki Unehara, Koichi Yamada, Takagi Hideyuki, Evaluation of Hybrid Optimization With EMO and IEC for Architectural Floor Planning, Joint 7th Int. Conf. on Soft Computing and Intelligent Systems and 15th Int. Symposium on Advanced Intelligent Systems (SCIS-ISIS2014), 2014.12, We investigate the combinatorial effect of evolutionary multi-objective optimization (EMO) with interactive evolutionary computation (IEC). The purposes and combination ways of several presented EMO and IEC researches are different. We evaluated seven combination ways of four EMO objectives given by fitness functions and one IEC objective given by a pseudo-IEC user outputting stable evaluation regardless repeated experiments in our previous experiments. In this paper, we extend experimental conditions to 39 and evaluate them: 3 pseudo-users × 13 combination ways of 4 + 1 objectives. We also consider features of this system. .
26. Takagi Hideyuki, Three Research Directions of Interactive Evolutionary Computation, 18th Online World Conference on Soft-Computing in Industrial Applications (WSC18), 2014.12, [URL], We overview three research directions of interactive evolutionary computation (IEC). The first direction is to extend IEC applications that are hard or impossible for other optimization approaches. The second one is to reduce unavoidable IEC user fatigue by improving IEC user interface, developing new evolutionary computation (EC) algorithms and EC operations that converge faster and are effective under the restricted IEC conditions, introducing an IEC user evaluation model, letting an IEC user intervene EC search, and others. The third one is a new research and is to use IEC as a tool for analyzing human characteristics indirectly by analyzing characteristics of the target system optimized based on an IEC user's psychological evaluation scale, which is somehow similar to reverse engineering..
27. Yan Pei, Hideyuki Takagi, Qiangfu Zhao, Yong Liu, A comprehensive analysis on optimization performance of chaotic evolution and its parameter distribution, IEEE Int. Conf. on Systems, Man, and Cybernetics (SMC 2014), 2014.10, In this paper, we analyse and discuss the relationship between optimization performance of chaotic evolution (CE) algorithm and distribution characteristic of chaotic parameter. CE is an evolutionary computation algorithm that simulates chaotic motion of a chaotic system in a search space for implementing optimization. However, its optimization performance, internal process mechanism and optimization principle are not well studied. In this paper, we investigate distribution characteristics of chaotic systems, which support chaotic parameter in CE algorithm. Compared with other two parameter generators, i.e., a quadratic-like random generator and an uniform random generator, CE algorithm with chaotic parameter generated by the logistic map ($\mu = 4$) shows better optimization performance significantly. We also make an algorithm comparison with differential evolution and an algorithm ranking by Friedman test and Bonferroni-Dunn test. The related topics on relationship between optimization performance of CE algorithm and chaotic parameter distribution are analysed and discussed. From these analyses and discussions, it indicates that chaotic parameter distribution is a significant factor that influences optimization performance of CE algorithm..
28. Makoto Inoue, Megumu Hiramoto, Muneyuki Unehara, Koichi Yamada, Hideyuki Takagi,
"Study of Combination of Evolutionary Multi-objective Optimization with Interactive Evolutionary Computation Using Pseudo-user
― Evaluation experiments with architecture floor plan as task ―"
30th Fuzzy System Symposium, Kouchi, Japan, pp.XXX-YYY (September 1-3, 2014) (in Japanese)..
29. 裴岩, 髙木 英行, Local Information of Fitness Landscape Obtained by Paired Comparison-based Memetic Search for Interactive Differential Evolution, 第7回 進化計算学会進化計算研究会, 2014.08.
30. 船木亮平, 髙木 英行, 中川尚志, 永田里恵, Nozomu Matsumoto, 人工内耳パラメータフィッティングへの対比較ベース対話型差分進化の適用, 第6回 進化計算学会進化計算研究会, 2014.03.
31. Koichi HATAE and Hideyuki TAKAGI, "Motion Pose Design Support System based on Interactive Differential Evolution," 15th SOFT Kyushu Chapter Meeting, Shimonoseki, Japan, pp.103-106 (December 21, 2013). (in Japanese)..
32. 裴岩, 髙木 英行, Method for determining search states of Markov Chain practically and its application to predict EC convergence and proof it, 2013進化計算シンポジウム, 2013.12.
33. PEI Yan, Takagi Hideyuki, Fitness Landscape Approximation by Adaptive Support Vector Regression with Opposition-Based Learning, IEEE Int. Conf. on Systems, Man, and Cybernetics (SMC 2013), 2013.10, We propose a method for approximating a fitness landscape using adaptive support vector regression (SVR) with opposition based learning (OBL) to enhance the evolutionary search. This method tries to resolve the complexity of the fitness landscape in the original search space by designing a suitable kernel function with an adaptive parameter tuned by OBL; This kernel projects the original search space into a higher dimensional search space with a different topological structure. The elite is obtained from the approximated fitness landscape, using the adaptive SVR to accelerate the evolutionary computation (EC) search, and the individual with the worst fitness is replaced. The merits of the proposed method are evaluated by comparing it with the fitness landscape approximated in the original, in a lower and in a higher dimensional search space.
34. Takagi Hideyuki, Overview of Our Research on Interactive Evolutionary Computation, Japan-Finland Joint Seminar 2013, 2013.06, [URL], We summarize our research on interactive evolutionary computation (IEC) in this decade. Our major IEC research directions are (A) expanding IEC applications in new areas to show wide applicability of IEC, (B) reducing IEC user fatigue to make IEC more practical, (C) developing new IEC frameworks, and (D) analyzing human characteristics using IEC. Among them, we introduce some our recent works in (B) - (D): development of algorithms for accelerating EC search, paired comparison-based interactive differential evolution, IEC with evolutionary multi-objective optimization, and IEC for human science..
35. Takagi Hideyuki, Statistical Tests for Computational Intelligence Research and Human Subjective Tests, 2013 IEEE Symposium Series on Computational Intelligence, 2013.04, [URL], See at
36. Takagi Hideyuki, Interactive evolutionary computation as a tool for human science, 九州大学芸術工学研究院応用知覚科学研究センター, 2013.04.
37. Hideyuki Takagi and Yan Pei, “Proposal of a Method for Accelerating Transition from Exploration to Exploitation”, Technical Report, 4th Evolutionary Computing meeting, Yokosuka, Japan, pp.96-101 (18-19, Mar., 2013) (in Japanese).
38. Masashi Ishizu and Hideyuki Takagi, "Study on Optimizing Color Scheme of a Subway Map using Interactive Differential Evolution," Angle SOFT Kyushu Chapter meeting, Fukuoka, Japan, pp.5-6 (March 2, 2013). (in Japanese).
39. PEI Yan, Takagi Hideyuki, Triple and Quadruple Comparison-Based Interactive Differential Evolution and Differential Evolution, Foundations of Genetic Algorithms XII (FOGA) 2013 Workshop, 2013.01.
40. PEI Yan, Takagi Hideyuki, Fitness Landscape Approximation by Adaptive Support Vector Regression with Opposition-Based Learning, Satellite Workshop on Problem, Landscape Analysis, Automated Algorithm Selection and Adaptation in Optimization at Foundations of Genetic Algorithms (FOGA2013) Workshop XII, 2013.01, We introduce two techniques to approximate and analyze fitness landscape for evolutionary search enhancement. One involves dimensionality reduction method used for fitness landscape approximation to reduce the computational complexity of the fitting. The other uses Fourier transform to obtain the frequency information of fitness landscape for search acceleration and multi-modal optimization. We briefly describe the inspirations, principles and results of the two techniques..
41. Anak Agung Gede Dharma, Hideyuki Takagi,Kiyoshi Tomimatsu, "Interactive neural network – Differential evolution framework for haptic feedback retrieval system," 2012 Evolutionary Computation Symposium, Karuizawa, pp.292-297 (December. 15-16, 2012)..
42. Yan Pei and Hideyuki Takagi, "Approximating and analyzing fitness landscape for evolutionary search enhancement,'' 14th SOFT Kyushu Chapter Meeting, Saga, pp. 21-24 (December 8, 2012). (in Japanese)..
43. PEI Yan, ZHENG Shaoqiu, TAN Ying, Takagi Hideyuki, An empirical study on influence of approximation approaches on enhancing fireworks algorithm, EEE Int. Conf. on Systems, Man, and Cybernetics (SMC 2012), 2012.10.
44. MA JingYe, 髙木 英行, Design of composite image filters using interactive genetic programming, 3rd Int. Conf. on Innovations in Bio-Inspired Computing and Applications, 2012.09.
45. Yan Pei and Hideyuki Takagi, "Triple and Quadruple Comparison-Based Interactive Differential Evolution and Differential Evolution," 3rd Evolutionary Computation Meeting, Higasi-Hiroshima, Japan, pp.74-84 (Sept. 3-4,2012) (in Japanese)..
46. Takagi Hideyuki, Introduction to Computational Intelligence and Interactive Evolutionary Computation,, 2012 Cybernetics Summer School (CSS2012), 2012.08.
47. PEI Yan, Takagi Hideyuki, Comparative study on fitness landscape approximation with Fourier transform, 6th Int. Conf. on Genetic and Evolutionary Computing (ICGEC2012), 2012.08.
48. PEI Yan, Takagi Hideyuki, Fourier analysis of the fitness landscape for evolutionary search acceleration, 2012 IEEE Congress on Evolutionary Computation, 2012.06.
49. Jingye Ma and Hideyuki Takagi, "Design of Integrated Image Filters Using Interactive Genetic Programming," Joint meeting of 2nd Evolutionary Computation Meeting and 6th Evolutionary Computation Frontier Meeting, Toyonaka, Japan, pp.106--111 (March 9-10,2012) (in Japanese)..
50. Sonny Alves Dias, Yuka Inokuchi, and Hideyuki Takagi, "Evolving a Human Player Model for the Star Trek Game," Joint meeting of 2nd Evolutionary Computation Meeting and 6th Evolutionary Computation Frontier Meeting, Toyonaka, Japan,,pp.112--117 (March 9-10,2012) (in Japanese)..
51. Ryohei Funaki and Hideyuki Takagi, "Complementary Effect of IDE/gravity and IDE/moving vector," Joint meeting of 2nd Evolutionary Computation Meeting and 6th Evolutionary Computation Frontier Meeting, Toyonaka, Japan, pp.161--166 (March 9-10,2012) (in Japanese)..
52. Yan Pei and Hideyuki Takagi, "Fourier Niching Approach for Multi-modal Optimization," Joint meeting of 2nd Evolutionary Computation Meeting and 6th Evolutionary Computation Frontier Meeting, Toyonaka, Japan, pp.189--195 (March 9-10,2012) (in Japanese)..
53. yohei Funaki and Hideyuki Takagi, ”Analysis of DE/gravity for Accelerating Interactive Differential Evolution on the Relation Between Convergence Characteristics and Global Optimum Locations,” 2011 Evolutionary Computation Symposium, Iwanuma, Japan, pp.103-110 (Dec. 17-18, 2011) (in Japanese).
54. Yan Pei and Hideyuki Takagi, "Fourier Analysis of Fitness Landscape to Accelerate Evolutionary Search," 2011 Evolutionary Computation Symposium, Iwanuma, Japan, pp.167--173 (Dec. 17-18, 2011) (in Japanese)..
55. Anak Agung Gede DHARMA, Hideyuki Takagi, and Kiyoshi Tomimatsu, "Emotional Expressions of Vibrotactile Haptic Message Designed by Paired Comparison-based Interactive Differential Evolution," 2011 Evolutionary Computation Symposium, Iwanuma, Japan, pp.247--252 (Dec. 17-18, 2011) (in Japanese)..
56. Research on Accelerating IEC and IEC Applications for Human Science.
57. Accelerating Evolutionary Computation with Elite Obtained by Dimensionality Reduction.
58. cceleration of Differential Evolution and Interactive Differential Evolution Using the Gravity Vector and the Average Moving Vector of Individuals.
59. Acceleration of Interactive Differential Evolution.
60. Proposal of Fewer-Fixed-Objective Combination for Evolutionary Many-Objective Optimization and its Evaluation.
61. , [URL].
62. Proposal of a Method for Accelerating Differential Evolution.
63. , [URL].
64. Combination of Interactive Evolutionary Computation and\\Evolutionary Multi-objective Optimization for Architectural Room Floor Planning.
65. Paired Comparison-based Interactive Differential Evolution.
66. , [URL].
67. Combinations of Some-Objective for Evolutionary Many-Objective Optimization.
68. Interactive Particle Swarm Optimization.
69. Relationship Between Similarity of Parallel IEC Users and Efficiency of Cooperative Parallel IEC Works.
70. Recent Topics of IEC Research.
71. Optimization of architectural room planning using methods of generating spatial layout plans and evolutionary multi-objective optimization: layout cases of six rooms and corridors for an apartment.
72. Signal processing based on audio-visual psychology and physiology.
73. Synthesizing handwritten characters using naturalness learning.
74. Computational Geometry Model for Evolutionary Spatial Planning.
75. Yu Nakano and Hideyuki Takagi, "Influence of Quantization Noise in Fitness Values to the Performance of Interactive PSO," Intelligent System Symposium (FAN Symposium), Nagoya, pp325-328 (August 29-31, 2007) (in Japanese).
76. Shigekazu Sugino and Hideyuki Takagi, "Designing Room Lighting Environment Using Interactive Evolutionary Computation," 23rd Fuzzy System Symposium,Nagoya, pp.585-588 (August 29-31, 2007) (in Japanese).
77. Hideyuki Takagi, "Toward Controlling Emotion of Video Movie Viewers," 21st Century COE Workshop on Psychological and Psychological Approach Toward Comfortableness, pp.28-33, (June 18, 2007) (in Japanese). .
78. Yu Nakano and Hideyuki Takagi, "Interactive particle swarm optimization," 8th SOFT Kyushu Chapter Meeting, KitaKyushu, Fukuoka, pp.27-30 (December 9, 2006) (in Japanese). .
79. Linfu LI, Hideyuki Takagi, Shoko Nagasaki, and Toshifumi Nakata, "Analysis of relationship between physical features of image media and viewer's physiological characteristics," 8th SOFT Kyushu Chapter Meeting, KitaKyushu, Fukuoka, pp.31-32 (December 9, 2006) (in Japanese). .
80. Kensuke Irie, Toshifumi Nakata, Iori Nakaoka, Linfu Li, and Hideyuki Takagi, "Extraction of Physical Features for Controlling Emotion of Video Movie Viewers," 22th Fuzzy System Symposium, Sapporo, pp.191--194 (Sept. 6--8, 2005) (in Japanese)..
81. Iori Nakaoka, Toshifumi Nakata, Kensuke Irie, Shoko Nagasaki, Linfu Li, and Hideyuki Takagi, "Speed Impression Model for Switching Speed of Movie Shot," 22th Fuzzy System Symposium, Sapporo, pp197--198 (Sept. 6--8, 2005) (in Japanese)..
82. Hideyuki Takagi, "Optimization Method for Designs Based on Preferences, KANSE, Experiences, and Physiological Responses,'' 21st COE Research Presentation, Tokyo, pp.26--27 (August 28, 2006) (in Japanese)..
83. Alexandra Melike Brintrup, Hideyuki Takagi, Jeremy Ramsden, "Evaluation of Sequential, Multi-objective, and Parallel Interactive Genetic Algorithms for Multi-objective Floor Plan Optimisation,'' EvoWorkshop2006, Budapest, Hungary, LNCS 3907, Springer-Verlag Berlin Heidelberg, pp.586--598 (April 10-12, 2006). .
84. Analysis of Evaluation Characteristics of Real Users to Realize IEC with Evaluation Characteristics of Multi-IEC Users.
85. Emotion control of Multimedia Audience Based on Interactive Evolutionary Computation and Physiological Analysis.
86. VR System that Minimize VR Sickness by Incorporating User's Personal Characteristics with Neural Networks.
87. Interactive Evolutionary Computation-based MEMS Design.
88. Shinya Henmi, Tadahiko Murata, and Hideyuki Takagi, "Interactive
evolutionary computation with evaluation characteristics of Multi-IEC Users -- Experimental evaluation through simulation," 21th Fuzzy System Symposium, Tokyo, pp189-192 (Sept. 7-9, 2005) (in Japanese)..
89. Shino Iwashita, Shangfei Wang, and Hideyuki Takagi, "Subjective evaluation on the method for reduction of IEC user's fatigue though rationg
scale mapping," 21th Fuzzy System Symposium, Tokyo, pp.610-613 (Sept.
7-9, 2005) (in Japanese)..
90. Raffi R. Kamalian, Ying Zhang, Hideyuki Takagi, and Alice M. Agogino, "Evolutionary Synthesis of Micromachines Using Supervisory Multiobjective Interactive Evolutionary Computation," Revised Selected Papers from ICMLC2005, LNAI 3930, Springer-Verlag Berlin Heidelberg, pp.429-437 (2006).
91. Hideyuki Takagi, Tomohiro Takahashi, Ken Aoki, Applicability of interactive evolutionary computation to mental health measurement, IEEE International Conference on Systems, Man, and Cybernetics (SMC2004), 2004.10, We show experimentally the applicability of interactive evolutionary computation (IEC) to a new application field, mental health measurement. We had 3 schizophrenics and 5 mentally healthy students design happy and sad impression computer graphics (CG) lighting images using IEC and asked other 33 students to evaluate the CG images using Scheffe’s method of paired comparison. Statistical tests of the evaluations showed that the range of emotional impressions perceived by the three schizophrenics between happy–sad was significantly narrower than that which was perceived by the mentally healthy students (p < 0.01). This experimental result implies that IEC has potential to contribute to psychiatry, and it also showed the possibility to expand IEC applicability beyond conventional system optimization and to a new area, mental health measurement.
Keywords: interactive evolutionary computation, schizophrenia, mental health measurement, CG lighting..
92. Meghna Babbar, Barbara Minsker, Hideyuki Takagi, Interactive Genetic Algorithm Framework for Long Term Groundwater Monitoring Design, World Water & Environmental Resources Congress 2004, 2004.06.
93. Meghna Babbar, Barbara Minsker, Takagi Hideyuki, Interactive Genetic Algorithm Framework for Long Term Groundwater Monitoring Design, World Water & Environmental Resources Congress 2004, 2004.06.
94. Hideyuki Takagi, Tomohiro Takahashi, and Ken Aoki, "Applicability of Interactive Evolutionary Computation to Mind Measurement," 20th Fuzzy System Symposium, Kitakyushu, pp.605-606 (June 2-5, 2004) (in Japanese)..
95. Tsuneo Kagawa, Hiroaki Nishino, Kouichi Utsumiya, and Hideyuki Takagi, "A method of 3D modeling using interactive evolutionary computation," 20th Fuzzy System Symposium, Kitakyushu, pp.697-700 (June 2-5, 2004) (in Japanese)..
96. Hideyuki Takagi, Norimasa Hayashida, Interactive EC-based Signal Processing, 4th Asia-Pacific Conference on Simulated Evolution And Learning (SEAL2002), 2002.11, We introduce new types of signal processing for which the characteristics of the signal processing filters are designed automatically by interactive evolutionary computation (IEC) based on human perception, such as hearing or vision. We first describe our existing works that use this approach, such as recovering distorted speech and hearing-aid fitting, as well as other related works in this field. Next, we evaluate the capabilities of visual-based image signal processing using IEC and compare it with conventional linear filters for the tasks of edge detection, high pass filtering, and horizontal / vertical component filtering. The experimental comparisons show that the performances of both methods are similar, which means that the new approach, without a priori knowledge on signal processing, is useful when signal processing users are not signal processing experts such as is the case in medical image processing or photo-retouch design..
97. Hiroaki Nishino, Hideyuki Takagi, Sung-Bae Cho, Kouichi Utsumiya, A 3D Modeling System for Creative Design, The 15th International Conference on Information Networking (ICOIN-15), 2001.01.
98. Norimasa Hayashida, Hideyuki Takagi, Visualized IEC: Interactive Evolutionary Computation with Multidimensional Data Visualization, IEEE International Conference on Industrial Electronics, Control and Instrumentation (IECON2000), 2000.10.
99. Hiroaki Nishino, Hideyuki Takagi, Kouichi Utsumiya, A Digital Prototyping System for Designing Novel 3D Geometries, 6th International Conference on Virtual Systems and MultiMedia (VSMM2000), 2000.10.
100. Toshihiko Noda, Dong Zhao, Hideyuki Takagi, Music Database Retrieval and Media Conversion System Based on Impression, 6th International Conference on Soft Computing (IIZUKA2000), 2000.10.
101. Hideyuki Takagi, Active User Intervention in an EC Search, International Conference on Information Sciences (JCIS2000,), 2000.02.
102. Hideyuki Takagi, Miho Ohsaki, IEC-based Hearing Aids Fitting, IEEE International Conference on System, Man, and Cybernetics (SMC'99), 1999.10.
103. Hideyuki Takagi, Toshihiko Noda, Sung-Bae Cho, Psychological Space to Hold Impression Among Media in Common for Media Database Retrieval System, IEEE International Conference on System, Man, and Cybernetics (SMC'99), 1999.10.
104. Hideyuki Takagi, Katsuhiro Kishi, On-line Knowledge Embedding for Interactive EC-based Montage System, Third International Conference on Knowledge-Based Intelligent Information Engineering Systems (KES'99), 1999.08.
105. Hideyuki Takagi, Sung-Bae Cho, Toshihiko Noda, Evaluation of an IGA-based Image Retrieval System Using Wavelet Coefficients, IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'99), 1999.08.
106. Takeo Ingu, Hideyuki Takagi, Accelerating a GA Convergence by Fitting a Single-Peak Function, IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'99), 1999.08.
107. Hideyuki Takagi, Shin'ichi Kamohara, Takashi Takeda, Introduction of Soft Computing Techniques to Welfare Equipment, 1999 IEEE Midnight-Sun Workshop on Soft Computing Methods in Industrial Applications (SMCia'99), 1999.06.
108. Hideyuki Takagi, Interactive Evolutionary Computation - Cooperation of computational intelligence and human KANSEI -, 5th International Conference on Soft Computing (IIZUKA'98), 1998.10, In this paper, we overview Interactive EC (evolutionary computation) research, showing the status quo and its remaining problems. The interactive EC technique optimizes systems from human interaction with computers. Recently, interest in this approach has increased in many application elds that we categorize into the artistic, engineering, and educational elds. We then overview the research within each eld. Finally, we show several trials to address the problem of human fatigue.
109. Hideyuki Takagi, Interactive Evolutionary Computation: System Optimization Based on Human Subjective Evaluation, IEEE International Conference on Intelligent Engineering Systems (INES'98), 1998.09.
110. Ken Aoki, Hideyuki Takagi, 3-D CG Lighting with an Interactive GA, 1st International Conference on Conventional and Knowledge-based Intelligent Electronic Systems (KES'97), 1997.05.
111. Tatsumi Watanabe, Hideyuki Takagi, Recovering System of the Distorted Speech using Interactive genetic Algorithms, IEEE International Conference on Systems, Man and Cybernetics (SMC'95), 1995.10.
112. Hideyuki Takagi, Michael H. Smith, Optimization of Fuzzy Systems by Switching Reasoning Methods Dynamically, 5th IFSA World Congress, 1993.07.
113. Hideyuki Takagi, Integrating Design Stage of Fuzzy System using Genetic Algorithms, IEEE 2nd International Conference on Fuzzy Systems (FUZZ-IEEE'93), 1993.03, This paper proposes an automaticfuzzy system design method that uses a Genetic Algorithm and integrates three design stages; our method determines membership functions, the number of fuzzy rules, and the ruleconsequent parameters at the same time. Because these design stages may not be independent, it is important to consider them simultaneously to obtain optimal fuzzy systems. The method includes a genetic algorithm and a penalty strategy that favors systems with fewer rules. The proposed method is applied to the classic inverted pendulum control problem and has been shown to be practical through a comparison with another method..