|Hideyuki Takagi||Last modified date：2021.05.21|
Professor / Modeling and Optimization / Department of Human Science / Faculty of Design
|1.||Jun Yu, Yuhao Li, Yan Pei, Hideyuki Takagi, Accelerating Evolutionary Computation Using a Convergence Point Estimated by Weighted Moving Vectors, Complex & Intelligent Systems, 10.1007/s40747-019-0111-6, 1-11, Jun Yu, Yuhao Li, Yan Pei, and Hideyuki Takagi, "Accelerating Evolutionary Computation Using a Convergence Point Estimated by Weighted Moving Vectors," Complex and Intelligent Systems, Springer International Publishing, pp.1-11 (May 28, 2019)., 2019.05, [URL], We introduce weighted moving vectors to increase the accuracy of estimating a convergence point of population and evaluateits efficiency. Key point is to weight moving vectors according to their reliability when a convergence point is calculatedinstead of equal weighting of the original method. We propose two different methods to evaluate the reliability of movingvectors. The first approach uses the fitness gradient information between starting points and terminal points of moving vectorsfor their weights. When a fitness gradient is bigger, the direction of a moving vector may have more potential, and a higherweight is given to it. The second one uses the fitness of parents, i.e., starting points of moving vectors, to give weights formoving vectors. Because an individual with higher fitness may have a high probability of being close to the optimal area, itshould be given a higher weight, vice versa. If the estimated point is better than the worst individual in current population, itis used as an elite individual and replace the worst one to accelerate the convergence of evolutionary algorithms. To evaluatethe performance of our proposal, we employ differential evolution and particle swarm optimization as baseline algorithms inour evaluation experiments and run them on 28 benchmark functions from CEC 2013. The experimental results confirmedthat introducing weights can further improve the accuracy of an estimated convergence point, which helps to make EC searchfaster. Finally, some open topics are given to discuss..|
|2.||Yan Pei, Jun Yu, Hideyuki Takagi, Search Acceleration of Evolutionary Multi-objective Optimization Using an Estimated Convergence Point, Mathematics, https://doi.org/10.3390/math7020129, 7, 2, 129-147, Yan Pei, Jun Yu, and Hideyuki Takagi, "Search Acceleration of Evolutionary Multi-objective Optimization Using an Estimated Convergence Point," Mathematics, vol.7, no. 2, pp.129-147 (on-line) (Jan., 2019)., 2019.01, [URL], We propose a method to accelerate evolutionary multi-objective optimization (EMO) search using an estimated convergence point. Pareto improvement from the last generation to the current generation supports information of promising Pareto solution areas in both an objective space and a parameter space. We use this information to construct a set of moving vectors and estimate a non-dominated Pareto point from these moving vectors. In this work, we attempt to use different methods for constructing moving vectors, and use the convergence point estimated by using the moving vectors to accelerate EMO search. From our evaluation results, we found that the landscape of Pareto improvement has a uni-modal distribution characteristic in an objective space, and has a multi-modal distribution characteristic in a parameter space. Our proposed method can enhance EMO search when the landscape of Pareto improvement has a uni-modal distribution characteristic in a parameter space, and by chance also does that when landscape of Pareto improvement has a multi-modal distribution characteristic in a parameter space. The proposed methods can not only obtain more Pareto solutions compared with the conventional non-dominant sorting genetic algorithm (NSGA)-II algorithm, but can also increase the diversity of Pareto solutions. This indicates that our proposed method can enhance the search capability of EMO in both Pareto dominance and solution diversity. We also found that the method of constructing moving vectors is a primary issue for the success of our proposed method. We analyze and discuss this method with several evaluation metrics and statistical tests. The proposed method has potential to enhance EMO embedding deterministic learning methods in stochastic optimization algorithms..|
|3.||Jun Yu, Hideyuki Takagi, Ying Tan, Multi-layer Explosion Based Fireworks Algorithm, International Journal of Swarm Intelligence and Evolutionary Computation, 10.4172/2090-4908.1000173, 7, 3, Jun Yu, Hideyuki Takagi, and Ying Tan, "Multi-layer Explosion Based Fireworks Algorithm," International Journal of Swarm Intelligence and Evolutionary Computation, vol.7, no.3 (December, 2018)., 2018.12, [URL], We propose a new multi-layer explosion strategy inspired by various explosion patterns of real reworks to accelerate reworks algorithm (FWA). Each rework individual conducts multiple explosions to explore a local fitness landscape carefully instead of a single layer explosion used in canonical FWA. In the proposal, each rework individual generates a small number of sparks in the first layer randomly, then the generated sparks conduct the second layer explosions to generate new diverse sparks. These new sparks repeat the above operations until the number of this iteration reaches the pre-de ned maximum layer number. Theoretically, the number of explosion layers can be set to any positive integer, and the proposed strategy expects to generate various potential sparks using the multi-layer explosion strategy without changing the total number of generated sparks. The proposed strategy can combine with not only basic FWA but also other versions of FWA algorithms easily and replace their corresponding explosion operations to develop a new version, multi-layer explosion-based FWA. To evaluate the performance of our proposal, we select a more powerful variant of FWA, Enhanced FWA (EFWA) as the baseline algorithm and combine with our proposed explosion strategy. We run our proposal on 28 benchmark functions from CEC2013 test suites of 2-dimensions (2-D), 10-D and 30-D with 30 trial runs and compare with several state-of-the art EC algorithms. The experimental results confirm that the proposed strategy is effective and promising, which can obtain a better performance for FWA in terms of convergence speed and convergence accuracy. We finally analyze composition as well as feasibility of proposal and list some open topics.
|4.||＠Yan Pei, ＠Hideyuki Takagi, Research progress survey on interactive evolutionary computation, Journal of Ambient Intelligence and Humanized Computing (on-line) https://doi.org/10.1007/s12652-018-0861-9, https://doi.org/10.1007/s12652-018-0861-9, Yan Pei and Hideyuki Takagi, "Research progress survey on interactive evolutionary computation," Journal of Ambient Intelligence and Humanized Computing, Springer Berlin Heidelberg (online) (May 23, 2018)., 2018.05, We report our research progress on interactive evolutionary computation (IEC). Following description of IEC features, we present our research on IEC user modeling, acceleration of IEC search, several IEC frameworks, evolutionary multi-objective optimization with IEC, and IEC for human science. IEC research is categorized into three fields in general; major part of IEC papers is IEC application-oriented research; almost all the others are research that aims to reduce IEC user fatigue; and very little work researches the use of IEC for human science. In particular, IEC is a data analysis and processing method and tool for the discovery of human psychological and physiological knowledge. We include several of our IEC application-oriented research projects in this paper, and focus on two other research directions, i.e., IEC algorithm research to reduce user fatigue, and IEC for human science research..|
|5.||Varunyu Vorachart, Hideyuki Takagi, Evolving fuzzy logic rule-based game player model for game development, International Journal of Innovative Computing, Information and Control, 10.24507/ijicic.13.06.1941, 13, 6, 1941-1951, Varunyu Vorachart and Hideyuki Takagi, "Evolving fuzzy logic rule-based game player model for game development," International Journal of Innovative Computing, Information and Control, vol.13, no.6, pp.1941--1951 (December, 2017)., 2017.12, [URL], We propose a framework for automatic game parameter tuning using a game player model. Two kinds of computational intelligence techniques are used to create the framework: a fuzzy logic system (FS) as the decision maker and evolutionary computation as the model parameter optimizer. Insights from a game developer are integrated into the player model consisting of FS rules. FS membership function parameters are optimized by a differential evolution (DE) algorithm to find optimal model parameters. We conducted experiments in which our player model plays a turn-based strategy video game. DE optimisation was able to evolve our player model such that it could compete well at various levels of game difficulty..|
|6.||Yan Pei, Hideyuki Takagi, Local fitness landscape from paired comparison-based memetic search in interactive differential evolution and differential evolution,, Int. J. Ad Hoc and Ubiquitous Computing, vol.25, no.s 1/2, 17-30, Int. J. Ad Hoc and Ubiquitous Computing, vol.25, no.s 1/2, pp.17-30 (2017)., 2017.03.|
|7.||Yan Pei, Shaoqiu Zheng, Ying Tan, Hideyuki Takagi, Effectiveness of approximation strategy in surrogate-assisted fireworks algorithm, International Journal of Machine Learning and Cybernetics, 10.1007/s13042-015-0388-8, 6, 5, 795-810, Yan Pei, Shaoqiu Zheng, Ying Tan and Hideyuki Takagi,
"Effectiveness of approximation strategy in surrogate-assisted fireworks algorithm,"
International Journal of Machine Learning and Cybernetics, vol. 6, no. 5, pp.795-810 (2015)., 2015.10, [URL], We investigate the effectiveness of approximation strategy in a surrogate-assisted fireworks algorithm, which obtains the elite from approximate fitness landscape to enhance its optimization performance. We study the effectiveness of approximation strategy from the aspects of approximation method, sampling data selection method and sampling size. We discuss and analyse the optimization performance of each method. For the approximation method, we use least square approximation, spline interpolation, Newton interpolation, and support vector regression to approximate fitness landscape of fireworks algorithm in projected lower dimensional, original and higher dimensional search space.
With regard to the sampling data selection method, we define three approaches, i.e., best sampling method, distance near the best fitness individual sampling method, and random sampling method to investigate each sampling method's performance. With regard to sample size, this is set as 3, 5, and 10 sampling data in both the approximation method and sampling method. We discuss and compare the optimization performance of each method using statistical tests. The advantages of the fireworks algorithm, a number of open topics, and new discoveries arising from evaluation results, such as multi-production mechanism of the fireworks algorithm, optimization performance of each method, elite rank, interpolation times and extrapolation times of elites are analysed and discussed..
|8.||Yan Pei, Hideyuki Takagi, Accelerating IEC and EC Searches with Elite Obtained by Dimensionality Reduction in Regression Spaces, Journal of Evolutionary Intelligence, Springer-Verlag Berlin Heidelberg, 10.1007/s12065-013-0088-9, 6, 1, 27-40, 2013.05, [URL], We propose a method for accelerating interactive evolutionary computation (IEC) and evolutionary computation (EC) searches using elite obtained in one-dimensional spaces and use benchmark functions to evaluate the proposed method. The method projects individuals onto n one-dimensional spaces corresponding to each of the n searching parameter axes, approximates each landscape using interpolation or an approximation method, finds the best coordinate from the approximated shape, obtains the elite by combining the best n found coordinates, and uses the elite for the next generation of the IEC or EC. The advantage of this method is that the elite may be easily obtained thanks to their projection onto each one-dimensional space and there is a higher possibility that the elite individual locates near the global optimum. We compare the proposal with methods for obtaining the landscape in the original search space, and show that our proposed method can significantly save computational time. Experimental evaluations of the technique with differential evolution using a simulated IEC user (Gaussian mixture model with different dimensions) and 34 benchmark functions show that the proposed method substantially accelerates IEC and EC searches..|
|10.||Hideyuki Takagi, Interactive evolutionary computation for analyzing human aware mechanism, Applied Computational Intelligence and Soft Computing, 10.1155/2012/694836, 2012, 1-8, Vol. 2012, pp.1-8, Article ID 694836 (2012)., 2012.06, [URL], We appeal the importance of establishing awareness science and show idea of using interactive evolutionary computation (IEC) as a tool for analyzing awareness mechanism and making awareness models. First, we describe the importance of human factors in computational intelligence and that IEC is one of approaches for so-called humanized computational intelligence. Second, we show examples that IEC is used as an analysis tool for human science. As analyzing human awareness mechanism is in this kind of analyzing human characteristics and capabilities, IEC may be able to be used for this purpose. Based on this expectation, we express one idea for analyzing the awareness mechanism. This idea is to make an equivalent model of an IEC user using a learning model and find latent variables that connect inputs and outputs of the user model and that help to understand or explain the inputs-outputs relationship. Although there must be several definitions of awareness, this idea is based on one definition that awareness is to find out unknown variables that helps our understanding. If we establish a method for finding the latent variables automatically, we can realize an awareness model in computer..|
|11.||Jan Dolinsky, Hideyuki Takagi, Analysis and Modeling of Naturalness in Handwritten Characters, IEEE Transactions on Neural Networks,, 10.1109/TNN.2009.2026174, 20, 10, 1540-1553, vol.20, no.10, pp.1540-1553 (2001), 2009.10.|
|12.||Architectural room planning support system using methods of generating spatial layout plans and evolutionary multi-objective optimization.|
|13.||Alexandra Melike Brintrup, Jeremy Ramsden, Hideyuki Takagi, Ashutosh Tiwari, Ergonomic Chair Design by Fusing Qualitative and Quantitative Criteria using Interactive Genetic Algorithms, IEEE Transaction on Evolutionary Computation , vol.12, no.3,pp.343-354, 2008.06.|
|14.||Jan Dolinsky and Hideyuki Takagi, RNN With a Recurrent Output Layer for Learning of Naturalness, Neural Information Processing - Letters & Reviews , vol.12, no.1-3, pp.31-42, 2008.01.|
|15.||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.|
|16.||Alexandra Melike Brintrup, Hideyuki Takagi, Ashutosh Tiwari and Jeremy J. Ramsden, Evaluation of sequential, multi-objective, and parallel interactive genetic algorithms for multi-objective optimization problems, Journal of Biological Physics and Chemist, Vol.6, pp.137-146 , 2006.12.|
|17.||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, LNAI 3930, Springer-Verlag Berlin
Heidelberg, pp.429-437 (2006), 2006.01.
|18.||Shangfei Wang and Hideyuki Takagi, Improving the Performance of Predicting Users' Subjective Evaluation Characteristics to Reduce Their Fatigue in IEC, Journal of Physiological Anthropology and Applied Human Science, Vol.24, No.1,pp.81-85, 2005.01.|
|19.||Hideyuki Takagi, Shaingfei Wang, and Shota Nakano, Proposal for a Framework for Optimizing Artificial Environments Based on Physiological Feedback, Journal of Physiological Anthropology and Applied Human Science, Vol.24, No.1,pp.77-80, 2005.01.|
|20.||Nobuhisa Tanaka and Hideyuki Takagi, Virtual Reality Environment Design of Managing Both Presence and Virtual Reality Sickness, Journal of Physiological Anthropology and Applied Human Science, Vol. 23, No. 6,pp.313-317, 2004.11.|
|21.||Jun-ichi Koga, Naoto Kawasaki, Shigekiyo Fujii, Hideyuki Takagi, "Simple Analog Filter to Improve the Voice Articulation of Mobile Phones and PHS Under Noisy Environment," Trans. of IEICE, vol.J87-A, no.7, pp.881-889 (2004) (in Japanese)..|
|22.||Kei Ohnishi and Hideyuki Takagi, "Numerical Optimization Based on Competition and Evolution among Generating Mechanisms for Search Points," IPSJ Transactions on Mathematical Modeling and Its Applications, vol.45, no.SIG 2(TOM 10), pp.32-41 (2004) (in Japanese)..|
|23.||Hideyuki Takagi and Toshihiko Noda, Media Converter with Impression Preservation Using a Neuro-Genetic Approach, International Journal of Hybrid Intelligent Systems, vol.1, no.1, pp.49-56, 2004.01.|
|24.||Hideyuki Takagi, Takeo Ingu, and Kei Ohnishi, "Accelerating a GA Convergence by Fitting a Single-Peak Function," J. of Japan Society for Fuzzy Theory and Intelligent Informatics, vol.15, no.2, pp.219-229 (2003) (in Japanese)..|
|25.||Hideyuki Takagi, Norimasa Hayashida, Acceleration of EC Convergence with Landscape Visualization and Human Intervention, Applied Soft Computing, 1, 4F, 245-256, 2002.10, We propose Visualized EC/IEC as an evolutionary computation (EC) and interactive EC (IEC) with visualizing individuals in a multi-dimensional searching space in a 2-D space. This visualization helps us envision the landscape of an n-D searching space, so that it is easier for us to join an EC search by indicating the possible global optimum estimated in the 2-D mapped space. We first compare four mapping methods from the points of view of computational time, convergence speed, and visual easiness to grasp whole EC landscape with five benchmark functions and 28 subjects. Then, we choose self-organizing maps for the projection of individuals onto a 2-D space and experimentally evaluate the effect of visualization using a benchmark function. The experimental result shows that the convergence speed of GA with human search on the Visualized space is at least five times faster than a conventional GA. © 2002 Elsevier Science B.V. All rights reserved.
Keywords: Interactive evolutionary computation; Multi-dimensional data visualization; Human intervention; Accelerating EC convergence; Human fatigue; Self-organizing maps.
|26.||Hiroaki Nishino, Hideyuki Takagi, and Kouich Utsumiya, "A 3D Modeler for Aiding Creative Work Using Interactive Evolutionary Computation," Trans. of IEICE, Vol.J85-D-II, No.9, pp.1473--1483 (2002) (in Japanese)..|
|27.||Norimasa Hayashida and Hideyuki Takagi, Acceleration of EC Convergence with Landscape Visualization and Human Intervention, Applied Soft Computing,, Vol. 1, No. 4F, pp. 245-256, Elsevier Science, 2002.01.|
|28.||Hideyuki Takagi, Interactive Evolutionary Computation: Fusion of the Capabilities of EC Optimization and Human Evaluation, Proceedings of the IEEE, 89, 9, 1275-1296, 2001.10, In this paper, we survey the research on interactive evolutionary computation (IEC). The IEC is an EC that optimizes systems based on subjective human evaluation. The definition and features of the IEC are first described and then followed by an overview of the IEC research. The overview primarily consists of application research and interface research. In this survey, the IEC application fields include graphic arts and animation, 3-D CG lighting, music, editorial design, industrial design, facial image generation, speech processing and synthesis, hearing aid fitting, virtual reality, media database retrieval, data mining, image processing, control and robotics, food industry, geophysics, education, entertainment, social system, and so on. Also in this survey, the interface research to reduce human fatigue includes improving fitness input interfaces and displays based on fitness prediction, accelerating EC convergence especially in early EC generations, examining combinations of interactive and normal EC, and investigating active user intervention. Finally, we discuss the IEC from the point of the future research direction of computational intelligence. In order to show the status quo IEC research, this paper primarily features a survey of about 250 IEC research papers rather than a carefully selected representation of a few papers..|
|29.||Hideyuki Takagi, Interactive Evolutionary Computation: Fusion of the Capacities of EC Optimization and Human Evaluation, Proceedings of the IEEE,, Vol. 89, No. 9, pp. 1275-1296, 2001.09.|
|30.||Hideyuki Takagi, Fusion Technology of Neural Networks and Fuzzy Systems: A Chronicled Progression from the Laboratory to Our Daily Lives, International Journal of Applied Mathematics and Computer Science, 10, 4, 647-673, 2000.10, We chronicle the research on the fusion technology of neural networks and fuzzy systems (NN+FS), the models that have been proposed from this research, and the commercial products and industrial systems that have adopted these models. First, we review the NN+FS research activity during the early stages in Japan, the US, and Europe. Next, following the classification of NN+FS models, we show the ease of fusing these technologies based on the similarities of the data flow network structures and the non-linearity realization strategies of NNs and FSs. Then, we describe several models and applications of NN+FS. Finally, we introduce some important and recently developed NN+FS patents..|
|31.||Shinichi Kamohara, Yutaka Ichinose, Takashi Takeda, and Hideyuki Takagi, Construction of Virtual Reality System for Arm Wrestling with Interactive Evolutionary Computation, J. of Robotics and Mechatronics, Vol.12, No.1, pp.48-52, 2000.01.|
|32.||Shinichi Kamohara, Takashi Yamada, Yutaka Ichinose, Takashi Takeda, and Hideyuki Takagi, Rehabilitation Support by Multiaxis Force Display, J. of Robotics and Mechatronics, Vol.12, No.1, pp.53-59, 2000.01.|
|33.||Eiji Mizutani, Hideyuki Takagi, David M. Auslander, and Jyh-Shing Roger Jang, Evolving Color Recipe, IEEE Trans. on System, Man, and Cybernetics, Part C, Vol.30, No.4, pp.537-550, 2000.01.|
|34.||Hideyuki Takagi, Fusion Technology of Neural Networks and Fuzzy Systems: A Chronicled Progression from the Laboratory to Our Daily Lives, International Journal of Applied Mathematics and Computer Science,, Vol.10, No.4, pp.647-673, 2000.01.|
|35.||Miho Ohsaki, Hideyuki Takagi, and Kimiko Ohya, An Input Method Using Discrete Fitness Values for Interactive GA, J. of Intelligent and Fuzzy Systems, vol.6, no.1, pp.131-145, 1998.01.|
|36.||Ken Aoki and Hideyuki Takagi, "Interactive GA-based Design Support System for Lighting Design in 3-D Computer Graphics," Trans. of IEICE, Vol.J81-DII, No.7, pp.1601-1608 (1998) (in Japanese)..|
|37.||Miho Ohsaki and Hideyuki Takagi, "Reduction of the Burden of Human Interactive EC Operators - Improvement of present interface by prediction of evaluation order -," J. of Japan Society for Artificial Intelligence, vol.13, no.5, pp.712-719 (1998) (in Japanese)..|
|38.||Hideyuki Takagi and Takashi Takeda, "Movement Models of Head and Eyes for Computer Graphics," Trans. of IEICE, Vol.J80-A, No.8, pp.1304-1311 (Aug., 1997) (in Japanese)
Electronics and Communications in Japan, Part 3, vol.82, no.1, pp.41-50, Script Tecnica (1999) (English Translation).
|39.||Huihua Liu, Hideyuki Takagi, Eiichi Tsuboka, Ditang Fang, and Nanming Zhao,
``A Method for Computation of Auditory Lateral Inhibition Parameters,''
Acta Biophysica Sinica，vol.12, no.4, pp.609--612 (December, 1996) (in Chinese)..
|40.||Kazuo Asakawa and Hideyuki Takagi, Neural Networks in Japan, Communications of the ACM, Vol.37, No.3, pp.106-112, 1994.01.|
|41.||Hideyuki Takagi, Noriyuki Suzuki, Toshiyuki Kouda, and Yoshinori Kojima, Neural-networks designed on Approximate Reasoning Architecture and Its Applications, IEEE Trans. on Neural Networks, Vol.3, No.5, pp.752-760, 1992.01.|
|42.||Hideyuki Takagi and Isao Hayashi, NN-driven Fuzzy Reasoning, Int. Journal of Approximate Reasoning (Special Issue of IIZUKA'88),, Vol.5, No.3, pp.191-213, 1991.01.|
|43.||Hideyuki Takagi, Toshiyuki Kouda, and Yoshihiro Kojima, "Neural-networks designed on Approximate Reasoning Architecture," J. of SOFT, Vol.3, No.1, pp.133-141 (1991) (in Japanese).|
|44.||Hideyuki Takagi, Shigeo Sakaue, and Hayato Togawa, "Evaluation of Non-linear Optimization Methods for the Learning Algorithm of Artificial Neural networks," Trans. of IEICE, Vol.J74-D-II, No.4, pp.528-535 (1991) (in Japanese).|
|45.||Hideyuki Takagi and Noriyuki Suzuki, "Application of Neural-networks designed on Approximate Reasoning Architecture to the Adjustment of VTR Tape-Running Mechanisms," J. of SOFT, Vol.3, No.4, pp.810-818 (1991) (in Japanese).|
|46.||Hideyuki Takagi, "Outer Surface Data Extraction Algorithm from Point Cluster in N-Dimensional Space and Its Applications," Trans. of IEICE, Vol.J74-D-II, No.11, pp.1617-1620 (1991) (in Japanese).|
|47.||Satoshi Kabasawa, Hideyuki Takagi, and Machiko Sannomiya, "Requirement for Practical Use of Voice Text-Entry for Word Processor," Trans. of IEICE, Vol.J70-D, No.11, pp.2115-2120 (1987) (in Japanese).|