Kyushu University Academic Staff Educational and Research Activities Database
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Hideyuki Takagi Last modified date:2019.09.25

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

Graduate School
Undergraduate School
Other Organization

Academic Degree
Doctor of Engineering
Country of degree conferring institution (Overseas)
Field of Specialization
computational intelligence
ORCID(Open Researcher and Contributor ID)
Total Priod of education and research career in the foreign country
Outline Activities
I have researched toward Humanized Computational Intelligence. I focus on interactive evolutionary computation (IEC) as an approach to this goal and have worked on increasing IEC performance, accelerating its search, reducing IEC user fatigue, and applying IEC for human science. Widely speaking, I am interested in fusing neural networks, fuzzy systems, and evolutionary computations as well as many other computational intelligence techniques in general. Fusing these techniques was my main research theme in late 1980's to early 1990's.
Research Interests
  • Humanized Computational Intelligence
    keyword : computational intelligence, evolutionary computation, interactive evolutionary computation, human factors
    1993.04Research topics include combination and fusion of human factor and computational intelligence and computational intelligence with/for human factors. I am mainly focusing on interactive evolutionary computation (IEC) as a tool for this research. The IEC research includes two research sub themes: applying this technique to wide variety of application fields and improving the IEC interface to reduce IEC user's fatigue. Some of our application fields include computer graphics, speech processing, hearing-aid fitting, virtual reality, geology, and therapy..
Current and Past Project
  • Grant-in-Aid for Scientific Research (18K11470).
  • Applying the estimation method of a convergence point in evolutionary computation to the firework algorithm. The estimation method and firework algorithm were developed by Kyushu University and Peking University, respectively.
  • Grant-in-Aid for Scientific Research (15K00340).
  • Grant-in-Aid for Scientific Research (23500279).
    Increasing interactive evolutionary computation (IEC) performance, finding new facts on auditory psychophysiology through applying IEC to cochlear implant fitting, and constructing an awareness model using IEC.
  • Development and evaluation of a room layout design support system that allows us to design room layout plans interactively based on our preference and experience.
  • We extract physical parameters from movies and sound that influence on physiological reaction. Once we can find such parameters, we may make multimedia whose such parameters are controlled to emphasize human physiological reactions such as thrilling, healing, or moved.
  • The dependency of our life to artificial environment has increased rapidly in recent years. Sometimes higher priority is given to convenience or economics for the artificial environment than human sensory characteristics. It results several troubles on our health; for example, human biorhythm is disturbed by artificial light, or troubles on our nervous or auditory system are caused by abused use of audio and visual signals.

    The objective of this project is to understand the human sensory characteristics based on physiological or psychological experimental research and to apply the obtained knowledge though the research to design of artificial environment. Concretely speaking, (1) we unify the knowledge on vision, hearing, smell, heat sense, and others from the environmental physiological and perceptual psychological point of view and (2) realize KANSE design on lighting, audio & visual, air conditioning, architecture based on the unified knowledge. We evaluate the artificial environment overall and establish a design guide for the artificial environment which is available not only normal people but also little children, elder persons, and handicapped persons.
Academic Activities
1. Hideyuki Takagi and Miho Ohsaki, Interactive Evolutionary Computation-Based Hearing-Aid Fitting, IEEE Transaction on Evolutionary Computation, vol.11, no.3, pp.414-427 (2007), 2007.06.
1. Yuhao Li, Jun Yu, Hideyuki Takagi, Ying Tan, Accelerating Fireworks Algorithm with Weight-based Guiding Sparks, 10th International Conference on Swarm Intelligence (ICSI2019), 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. 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..
5. 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..
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, 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..
8. 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..
9. Scouting Strategy Applied to Fireworks Algorithm.
Membership in Academic Society
  • The Japan Soceity of Evolutionary Computation
  • The Institute of Electronics, Information and Communication Engineers
  • The Acoustical Society of Japan
  • Japan Society for Fuzzy Theory and Intelligent Information
  • The Japanese Society for Artificial Intelligence
  • IEEE Most Active SMC Technical Committee Award
  • IEEE Most Active SMC Technical Committee Award
  • Best Paper Award for:
    Yan Pei and Hideyuki Takagi, "Comparative study on fitness landscape approximation with Fourier transform"
  • IEEE Most Active SMC Technical Committee Award
Educational Activities
For undergraduate education, I give course lectures for students of mainly Dept. of Art and Information Design at School of Design and supervise Bachelor research.
For graduate education, I give course lectures and several lab works for Master and Doctoral students at Graduate School of Design and supervise Master and Doctoral researchers.
Other Educational Activities
  • 2018.10, I organized the Meeting of Students and Young Researchers of SMC Taiwan-Japan Chapters in Miyazaki. Total 16 graduate students from Taiwan and Japan participated this educational event..
  • 2017.09, External Academic Advisory of Dept. of Electrical Engineering, Hong Kong City University in 2015 - 2017..
  • 2017.07, referee of a doctoral dissertation and opponent of its Ph.D. candidate (Hong Kong City University, July, 2017 - Sept., 2017).
  • 2013.03, referee of a doctoral dissertation and opponent of its Ph.D. candidate (Hong Kong City University, March, 2013 - July, 2013).
  • 2012.11, referee of a doctoral dissertation and opponent of its Ph.D. candidate (Hong Kong City University, Nov., 2012 - Feb., 2013).
  • 2012.08, opponent of doctoral dissertation defense (Technical University of Kosice, August 28, 2012).
  • 1999.06, opponent of doctoral dissertation defense (Helsinki University of Technology, June 21, 1999).
Professional and Outreach Activities
2018 - 2019: Director of Fuzzy Logic Systems Institute /
2018 - 2019: Research Ethics Committee member of Fuzzy Logic Systems Institute /
2016 - 2017: Director of Fuzzy Logic Systems Institute /
2016 - 2017: Research Ethics Committee member of Fuzzy Logic Systems Institute /
2014 - 2015: Director of Fuzzy Logic Systems Institute /
2014 - 2015: Research Ethics Committee member of Fuzzy Logic Systems Institute /
2012 - 2013: Director of Fuzzy Logic Systems Institute /
2012 - 2013: Research Ethics Committee member of Fuzzy Logic Systems Institute /
2010 - 2011: Director of Fuzzy Logic Systems Institute /
2008 - 2011: Research Ethics Committee member of Fuzzy Logic Systems Institute /
2002 - 2009: Councilor of Fuzzy Logic Systems Institute /
2004: Examinor of Academis-Industry R&D Support Committee, Kyushu Industrial Technology Center /
2001 - 2003: Examinor of Academis-Industry R&D Support Committee, Institute of Systems & Information Technologies/KYUSHU /.