Papers
Number of the published data : 117
No. Publishing type Authorship Title Author Journal Publisher Volume/issue/page Publication date ISSN DOI URL Summary
1 Research paper (international conference proceedings)
Joint
A New Binomial Crossover Considering Correlation Among Decision Variables for Adaptive Differential Evolution
Tetsuyuki Takahama and Setsuko Sakai
Proc. of Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems (SCIS&ISIS2018)

467-473
2018/12



In population-based optimization methods such as
evolutionary algorithms, various information can be obtained
from the distribution of good search points. When problems with
strong dependency among decision variables are optimized, a
characteristic distribution, which is a thin elliptical distribution,
may appear. In order to generate good children, it is necessary
to change the variables simultaneously along the long axis of
the elliptical distribution. A similar distribution also may appear
when the search points are far from the optimal solution even in
problems with independent variables. In this study, we propose a
new crossover CBX which uses correlation coefficients of search
points in order to detect such distribution and realizes efficient
movement toward the optimal solution. The crossover points are
decided so that highly correlated variables are inherited at the
same time. However, if only CBX is used, the diversity of the
search points tends to be lost rapidly. The adaptive control of
the probability for applying CBX is also proposed. The advantage
of the proposed method is shown by solving several benchmark
problems.
2 Research paper (scientific journal)
Joint
The Velocity Updating Rule According to an Oblique Coordinate System with Mutation and Dynamic Scaling for Particle Swarm Optimization
T.Takahama, S.Sakai
Artificial Life and Robotics

23/ 4, 618-627
2018/12



Particle swarm optimization (PSO) has been showing powerful search performance especially in separable and unimodal problems. However, the performance is deteriorated in non-separable problems such as rotated problems. In this study, a new velocity updating rule according to an oblique coordinate system, instead of an orthogonal coordinate system, is proposed to solve non-separable problems. Two mutation operations for the best particle and the worst particle are
proposed to improve the diversity of particles and to decrease the degradation of moving speed of particles. Also, the vectors generated according to the oblique coordinate system is dynamically scaled in order to improve the robustness and efficiency of the search. The advantage of the proposed method is shown by solving various problems including separable, non-separable, unimodal, and multimodal problems, and their rotated problems and by comparing the results of the proposed method with those of standard PSO.
3 Research paper (international conference proceedings)
Joint
Grouping of Genes According to Correlation Coefficients and Grouping-Based Crossover for Adaptive Differential Evolution
Tetsuyuki Takahama and Setsuko Sakai
The 50th ISCIE International Symposium on Stochastic Systems Theory and Its Applications (SSS'18), extended abstract


2018/11



When problems with strong dependency among deci-
sion variables are optimized, a characteristic distribu-
tion, which is a thin elliptical distribution, may appear.
In order to generate good children, it is necessary to
change the variables (genes) simultaneously along the
long axis of the elliptical distribution. Since binomial
crossover in differential evolution determines whether
each gene is crossed or not with the same probability,
it is difficult to change some genes simultaneously. In
this study, we propose a crossover operation GBX which
uses correlation coefficients of search points in order to
detect such distribution. The highly correlated genes
are grouped and the genes in each group are crossed
(or not crossed) simultaneously. However, if only GBX
is used, the diversity of the search points tends to be
lost rapidly. The adaptive control of the probability
for applying GBX is also proposed. The advantage of
the proposed method is shown by solving several bench-
mark problems.
4 Research paper (bulletin of university, research institution)
Joint
変数間依存性を解消する変換を導入したブレンド交叉の提案
阪井節子,高濱徹行
数理解析研究所講究録2078

65-72
2018/07



本研究では,変数依存性を弱めるために空間を変換し,変換された空間上で変数分離型問題に強いBLX-αを適用するTBLX-αを提案した.TBLX-αでは,まず個体集団の分散共分散行列にCholesky分解を適用し変換行列を求め,個体集団に変換行列を適用して変数間依存性のない空間に変換する.次に,変換空間においてを適用し子個体を求める.最後に子個体を元の空間に戻す.本手法を単峰性,多峰性,回転問題などを含む13個のテスト関数に適用した.拡張率αの調整を行うことにより変数間依存性が強い問題ではある程度の性能が得られたが,優れた性能を実現するには、さらに検討が必要であるという課題が残った.
5 Research paper (scientific journal)
Joint
Particle swarm optimisation with dynamic search strategies based on rank correlation
T.Nishio, J.Kushida, A.Hara, T.Takahama
International Journal of Computational Intelligence Studies

6/ 4, 311-332
2018/01




6 Research paper (international conference proceedings)
Joint
Particle Swarm Optimization with the Velocity Updating Rule According to an Oblique Coordinate System
Tetsuyuki Takahama and Setsuko Sakai
Proc. of the 2nd International Symposium on Swarm Behavior and Bio-Inspired Robotics (SWARM2017)

70-77
2017/10



Optimization problems have some characteristics: Dependency among decision variables such as separable or
non-separable problems, and landscape modality such as unimodal or multimodal problems. Particle swarm optimization (PSO) has been shown powerful search performance especially in separable and unimodal problems. However, the
performance is deteriorated in non-separable problems such as rotated problems. Although velocity updating rules using
random rotation matrices have been proposed to solve non-separable problems, the computational cost of generating the
random rotation matrices is very high. In this study, a new velocity updating rule according to an oblique coordinate
system, instead of an orthogonal coordinate system, is proposed to solve non-separable problems. Also, two mutation operations for the worst particle and the best particle are proposed to improve the diversity and the convergence of particles,
respectively. The advantage of the proposed method is shown by solving various problems including unimodal problems,
multimodal problems, and rotated problems and by comparing the results of the proposed method with those of standard
PSO.
7 Research paper (bulletin of university, research institution)
Joint
適応型差分進化JADEにおける個体順位に基づくグループ別パラメータ制御
阪井節子,高濱徹行
数理解析研究所講究録2044

159-170
2017/09




8 Research paper (international conference proceedings)
Joint
An Adaptive Differential Evolution with Exploitation and Exploration by Extreme Individuals
Tetsuyuki Takahama and Setsuko Sakai
Proc. of SICE Annual Conference 2017 (SICE2017)

1147-1152
2017/09



In a natural population, extreme individuals are very important for survival of the population. When the main
population is destroyed by catastrophes, the few extreme individuals gain significance and will insure the survival of the
population. Differential Evolution (DE) has been successfully applied to various optimization problems. However, DE
sometimes trapped into some local solutions, which is a kind of a catastrophe. In this study, the extreme individuals are
paid attention to. The best individuals perform exploitation or local search to keep extremely good positions. The worst
individuals perform exploration or global search to keep positions far from the best individuals. Other individuals perform
adaptive search based on JADE which is one of the most successful algorithms on controlling algorithm parameters. In
JADE, the values of two algorithm parameters are generated according to two probability density functions which are
learned by the values in success cases where the child is better than the parent. The advantage of JADE with exploitation
and exploration by extreme individuals is shown by solving thirteen benchmark problems.
9 Research paper (international conference proceedings)
Joint
An Adaptive Differential Evolution with Learning Parameters According to Groups Defined by the Rank of Objective Values
Tetsuyuki Takahama and Setsuko Sakai
Proc. of the Eighth International Conference on Swarm Intelligence (ICSI2017)

411-419
2017/07



Differential Evolution (DE) has been successfully applied to
various optimization problems. The performance of DE is affected by algorithm parameters such as a scaling factor F and a crossover rate CR.
Many studies have been done to control the parameters adaptively. One
of the most successful studies on controlling the parameters is JADE. In
JADE, the values of each parameter are generated according to one probability density function (PDF) which is learned by the values in success
cases where the child is better than the parent. However, search performance might be improved by learning multiple PDFs for each parameter
based on some characteristics of search points. In this study, search points
are divided into plural groups according to the rank of their objective
values and the PDFs are learned by parameter values in success cases for
each group. The advantage of JADE with the group-based learning is
shown by solving thirteen benchmark problems.
10 Research paper (scientific journal)
Joint
Particle Swarm Optimization with Mutation Operations Controlled by Landscape Modality Estimation using Hill-Valley Detection
T.Takahama, S.Sakai, J. Kuashida and A. Hara
Artificial Life and Robotics

21/ 4, 423-433
2016/12




11 Research paper (scientific journal)
Joint
Estimating Landscape Modality of Objective Functions using Rank Correlation for Evolutionary Algorithms
Jun-ichi Kushida, Akira Hara, Tetsuyuki Takahama
Journal of the Japanese Society for Evolutionary Computation

7/ 2, 32-45
2016/11




12 Research paper (international conference proceedings)
Joint
Deterministic Geometric Semantic Genetic Programming with Optimal Mate Selection
Akira Hara, Jun-ichi Kushida and Tetsuyuki Takahama
Proc. of 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC2016)

3387-3392
2016/10




13 Research paper (international conference proceedings)
Joint
An Adaptive Differential Evolution with Adaptive Archive Selection and Hill-Valley Detection
Tetsuyuki Takahama and Setsuko Sakai
Proc. of 2016 IEEE Congress on Evolutionary Computation (CEC2016)

48-55
2016/07



Differential Evolution (DE) has been successfully
applied to various optimization problems. The performance of
DE is affected by algorithm parameters. One of the most successful studies on parameter control is JADE. In JADE, the two parameter values are generated according to two probability density functions which are learned by the parameter values in success cases, where the child is better than the parent. Also, an optional external archive which consists of defeated parents is introduced to keep diversity. However, the effect of the archive much depends on the optimization problems. In this study, an adaptive method for using the archive is proposed where probability for selecting an operation with the archive or an operation without the archive is adaptively controlled based on the success probability of the operations. Also, hills and valleys in an objective function are detected in order to improve the performance of JADE. The efficiency and robustness of search process can be improved by adopting a small F for valley points and a large F for hill points. The effect of the proposed method is shown by solving thirteen benchmark problems.
14 Research paper (bulletin of university, research institution)
Joint
レーティングシステムを利用した差分進化によるコンピュータオセロプレイヤーの学習
阪井節子,高濱徹行
数理解析研究所講究録1990

136-145
2016/04




15 Research paper (scientific journal)
Joint
Improving an Adaptive Differential Evolution Using Hill-Valley Detection
Tetsuyuki Takahama and Setsuko Sakai
International Journal of Hybrid Intelligent Systems

13/ 1, 1-13
2016/03




16 Research paper (international conference proceedings)
Joint
Improving an Adaptive Differential Evolution Using Hill-Valley Detection
Tetsuyuki Takahama, Setsuko Sakai
Proc. of the 7th International Conference on Soft Computing and Pattern Recognition (SoCPaR2015)

284-289
2015/11



The performance of Differential Evolution (DE) is affected by algorithm parameters such as a scaling factor F and a crossover rate CR. Many studies have been done to control the parameters adaptively. One of the most successful studies on parameter control is JADE. In JADE, two parameter values are generated according to a probability density function which is learned by the parameter values in success cases, where the child is better than the parent. In this study, landscape of an objective function
is paid attention to in order to improve the performance of JADE.
The efficiency and robustness of search process can be improved
by detecting valleys and hills in search points and by adopting
a small F for valley points and a large F for hill points because
an optimal solution exists near valleys and far from hills in
minimization problems. Valley points and hill points are detected
by creating a proximity graph from search points and by selecting
valley/hill points that are smaller/greater than neighbor points.
The effect of the proposed method is shown by solving thirteen
benchmark problems.
17 Research paper (international conference proceedings)
Joint
Learning Game Players by an Evolutionary Approach Using Pairwise Comparison without Prior Knowledge
Tetsuyuki Takahama, Setsuko Sakai
Proc. of International Conference on Intelligent Informatics and BioMedical Sciences 2015 (ICIIBMS2015)

121-127
2015/11



There are many studies to learn an artificial game player or game strategy automatically or by unsupervised learning. One of representative methods for unsupervised learning of players is evolutionary learning approaches. In this study, an evolutionary approach with using pairwise comparison of two players is proposed to learn Othello players under the condition that the players only know the rules of the game. In order to solve a highly dynamic and unstable problem of learning players, Differential Evolution (DE) is adopted because DE adopts pairwise comparison and has been successfully applied to a highly uncertain and unstable problem of optimizing human preference. In our proposed method, players are randomly generated and form a population. Each player in the population is perturbed by DE operations and a child player is created. As pairwise comparison, the child plays against the parent player. The winner becomes a survivor. The population is replaced by the survivors. Players are evolved by repeating these processes. Computer simulation of Othello games is performed and it is shown that the method can generate good players who can win a standard heuristic player.
18 Research paper (international conference proceedings)
Joint
Genetic Programming Using the Best Individuals of Genealogies for Maintaining Population Diversity
Akira Hara, Takuya Mototsuka, Jun-ichi Kushida and Tetsuyuki Takahama
Proc. of 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC2015)

2690-2696
2015/10




19 Research paper (international conference proceedings)
Joint
Improving Particle Swarm Optimization by Estimating Landscape Modality Using a Proximity Graph
Tetsuyuki Takahama, Setsuko Sakai
Proc. of the First International Symposium on Swarm Behavior and Bio-Inspired Robotics (SWARM2015)

356-363
2015/10



In population-based optimization algorithms (POAs) such as particle swarm optimization (PSO), if landscape
modality of an objective function can be identified, strategies of the POAs can be selected properly. We have proposed
a method that estimates the landscape modality by sampling some points, but the method needs additional functional
evaluations for the sampling points. In this study, a new estimation method using a proximity graph, which does not need
additional evaluations, is proposed. A proper strategy is selected according to the landscape modality: The gbest model is
selected in unimodal landscape and the lbest model is selected in multimodal landscape. Also, two mutation operations for
unimodal and multimodal landscapes are proposed to update the worst solution. The advantage of the proposed method
is shown by solving various problems including unimodal and multimodal problems and by comparing the results of the
proposed method with those of the gbest and lbest model of PSO.
20 Research paper (international conference proceedings)
Joint
Emerging Collective Intelligence in Othello Players Evolved by Differential Evolution
Tetsuyuki Takahama, Setsuko Sakai
Proc. the 2015 IEEE Conference on Computational Intelligence and Games (CIG2015)

214-221
2015/08



The evaluation function for game playing is very
important. However, it is difficult to make a good evaluation
function. In this study, we propose to play Othello using collective
intelligence of players. The evaluation functions of the players
are learned or optimized by Differential Evolution. The objective
value is defined based on the total score of the games with a
standard Othello player. In order to generate different types of
players, the objective value is slightly changed by introducing the
stability of each player. Each player can select a next move using
the learned evaluation function. The collective intelligence player
selects a move based on majority vote where the move voted by
many players is selected. It is shown that the collective intelligence
is effective to game players through computer simulation.
21 Research paper (international conference proceedings)
Joint
An Adaptive Differential Evolution Considering Correlation of Two Algorithm Parameters
Tetsuyuki Takahama, Setsuko Sakai
Proc. of the Joint 7th International Conference on Soft Computing and Intelligent Systems and 15th International Symposium on Advanced Intelligent Systems (SCIS&ISIS 2014)

618-623
2014/12




22 Research paper (international conference proceedings)
Joint
Rank-based Semantic Control Crossover in Genetic Programming
Akira Hara, Jun-ichi Kushida, Takeyuki Nobuta and Tetsuyuki Takahama
Proc. of 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC2014)

515-520
2014/10




23 Research paper (scientific journal)
Joint
NCRDE: Improving Differential Evolution Based on Distance of Individuals and Ranking Information
Jun-ichi Kushida, Akira Hara, Tetsuyuki Takahama
The Transactions of the Institute of Electronics, Information and Communication Engineers (IEICE)

J97-D/ 10, 1604-1615
2014/10




24 Research paper (international conference proceedings)
Joint
Selecting Strategies in Particle Swarm Optimization by Sampling-Based Landscape Modality Detection using Inner Products
Tetsuyuki Takahama and Setsuko Sakai
Proc. of SICE Annual Conference 2014

1561-1566
2014/09



In population-based optimization algorithms (POAs) such as particle swarm optimization (PSO), if landscape
modality of an objective function can be identified, strategies of the POAs can be selected properly. We have proposed a
method that estimates the landscape modality by sampling some points along a line and counting the number of changes
in the objective values from increasing to decreasing and vice versa. In the method, the range of sampling on the line
cannot be decided when the width of the search points in a dimension is zero. In this study, we propose to determine
the range using inner products and also we propose to select a proper strategy according to the landscape modality: The
gbest model is selected in unimodal landscape and the lbest model is selected in multimodal landscape. Also, a simple
parameter selection for unimodal landscape is introduced. The advantage of the proposed method is shown by solving
various problems including unimodal and multimodal problems and by comparing the results of the proposed method
with those of the gbest and lbest model of PSO.
25 Research paper (international conference proceedings)
Joint
Selecting Strategies in Particle Swarm Optimization by Sampling-Based Landscape Modality Detection
Tetsuyuki Takahama and Setsuko Sakai
Proc. of the 2014 International Conference on Parallel and Distributed Processing Techniques and Applications

215-221
2014/07



If the landscape of the objective function is
unimodal, the efficiency of population-based optimization
algorithms (POAs) can be improved by selecting strategies
for local search around a best solution. If the landscape is
multimodal, the robustness of the POAs can be improved
by selecting strategies for global search in search space.
We have proposed a method that estimates the landscape
modality by sampling the objective values along a line and
counting the number of changes in the objective values
from increasing to decreasing and vice versa. In this study,
in order to improve the performance of particle swarm
optimization (PSO), we propose to select a proper strategy
according to the landscape modality: The gbest model is
selected in unimodal landscape and the lbest model is
selected in multimodal landscape. The advantage of the proposed method is shown by solving unimodal and multimodal
problems and by comparing it with standard PSOs.
26 Research paper (international conference proceedings)
Joint
Neuroevolution by Particle Swarm Optimization with Adaptive Input Selection for Controlling Platform-Game Agent
Akira Hara, Jun-ichi Kushida, Koji Kitao, and Tetsuyuki Takaham
Proc. of 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC2013)

2504-2509
2013/10




27 Research paper (international conference proceedings)
Joint
Efficient Constrained Optimization by the Epsilon Constrained Differential Evolution with Rough Approximation Using Kernel Regression
Tetsuyuki Takahama and Setsuko Sakai
Proc. of 2013 IEEE Congress on Evolutionary Computation (CEC2013)

1334-1341
2013/06



We have proposed a rough approximation model, which is an approximation model with low accuracy and without learning process, to reduce the number of function
evaluations in unconstrained optimization. Although the approximation errors between the true function values and the approximation values estimated by the rough approximation model are not small, the rough model can estimate the order relation of two points with fair accuracy. In order to use this nature of the rough
model, we have proposed estimated comparison which omits the
function evaluations when the result of comparison can be judged by approximation values. In this study, we propose to utilize the estimated comparison in constrained optimization and propose the eDEkr, which is the combination of the epsilon constrained method
and the estimated comparison using kernel regression. The eDEkr is a very efficient constrained optimization algorithm that can find high-quality solutions in very small number of function evaluations. It is shown that the "DEkr can find near optimal solutions stably in very small number of function evaluations compared with various other methods on well known nonlinear constrained problems.
28 Research paper (scientific journal)
Joint
Knowledge Acquisition from Many-Attribute Data by Genetic Programming with Clustered Terminal Symbols
A.Hara, H.Tanaka, T.Ichimura, and T.Takahama
International Journal of Knowledge and Web Intelligence

3/ 2, 180-201
2012/11




29 Research paper (international conference proceedings)
Joint
Heterogeneous Particle Swarm Optimization Including Predator-Prey Relationship
Akira Hara, Kazumasa Shiraga, Tetsuyuki Takahama
Proc. of The 6th International Conference on Soft Computing and Intelligent Systems & The 13th International Symposium on Advanced Intelligent Systems

1368-1373
2012/11




30 Research paper (international conference proceedings)
Joint
New Crossover Operator Based on Semantic Distance between Subtrees in Genetic Programming
Akira Hara, Yoshimasa Ueno, Tetsuyuki Takahama
Proc. of 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC2012)

721-726
2012/10




31 Research paper (international conference proceedings)
Joint
Differential Evolution with Graph-Based Speciation by Competitive Hebbian Rules
Tetsuyuki Takahama and Setsuko Sakai
Proc. of the Sixth International Conference on Genetic and Evolutionary Computing

445-448
2012/08




32 Research paper (international conference proceedings)
Joint
Large Scale Optimization by Differential Evolution
with Landscape Modality Detection and a Diversity Archive
Tetsuyuki Takahama and Setsuko Sakai
Proc. of 2012 IEEE Congress on Evolutionary Computation

2842-2849
2012/06



In this study, the performance of Differential Evolution
(DE) with landscape modality detection and a diversity archive (LMDEa) is reported on the set of benchmark functions provided for the CEC2012 Special Session on Large Scale Global Optimization. In DE, large population size which exceeds the problem dimensions largely, is adopted to keep the diversity of search. However, it is difficult to adopt such large size to solve large scaled optimization problems. In this study, we propose to solve large scale optimization problems using small population size and a large archive for diversity.
Also, we propose simple control of scaling factor by observing landscape modality of search points in order to keep diversity. In LMDEa, some points on a line connecting the centroid of search points and a search point are sampled. When the objective values of the sampled points are changed decreasingly and then increasingly, it is thought that one valley exists. If there exists only one valley, the landscape is unimodal and small scaling factor is adopted. Otherwise, large scaling factor is adopted. The effect of the proposed method is shown by solving the benchmark functions.
33 Research paper (international conference proceedings)
Joint
Efficient Constrained Optimization by
the epsilon Constrained Rank-Based Differential Evolution
Tetsuyuki Takahama and Setsuko Sakai
Proc. of 2012 IEEE Congress on Evolutionary Computation

62-69
2012/06



The epsilon constrained method is an algorithm transformation method, which can convert algorithms for unconstrained problems to algorithms for constrained problems using the epsilon level comparison, which compares search points based on the pair of objective value and constraint violation of them. We have proposed the epsilon constrained differential evolution eDE, which is the combination of the epsilon constrained method and differential evolution (DE). In this study, we propose the epsilon constrained rank-based DE (eRDE), which adopts a new and simple scheme of controlling algorithm parameters in DE. In the scheme, different parameter values are selected for each individual. Small scaling factor and large crossover
rate are selected for good individuals. Large scaling factor and small crossover rate are selected for bad individuals. The goodness is given by the ranking information. The eRDE can find high-quality solutions in very small number of function evaluations. It is shown that the eRDE can find near optimal solutions stably in about half the number of function evaluations compared with various other methods on well known nonlinear constrained problems.
34 Research paper (international conference proceedings)
Joint
Differential Evolution with Dynamic Strategy and Parameter Selection by Detecting Landscape Modality
Tetsuyuki Takahama and Setsuko Sakai
Proc. of 2012 IEEE Congress on Evolutionary Computation

2114-2121
2012/06



Differential Evolution (DE) is an evolutionary algorithm. DE has been successfully applied to optimization problems including non-linear, non-differentiable, non-convex and multimodal functions. There are several mutation strategies
such as the best and the rand strategy in DE. It is known
that the best strategy is suitable for unimodal problems and the rand strategy is suitable for multimodal problems. However, the landscape of a problem to be optimized is often unknown and the landscape is changing dynamically while the search process proceeds. In this study, we propose a new and simple method that detects the modality of landscape being searched: unimodal or not unimodal. In the method, some points on the line connecting the centroid of search points and the best search point are sampled.
When the objective values of the sampled points are changed
decreasingly and then increasingly, it is thought that one valley exists. If there exists only one valley, the landscape is unimodal and a greedy strategy like the best strategy is adopted. Otherwise, the rand strategy is adopted. The effect of the proposed method is shown by solving some benchmark problems.
35 Research paper (scientific journal)
Joint
RDE: Improving Differential Evolution By Using Ranking Information of Search Points
Tetsuyuki Takahama, Setsuko Sakai, Akira Hara
The Transactions of the Institute of Electronics, Information and Communication Engineers (IEICE)

J95-D/ 5, 1196-1205
2012/05




36 Research paper (scientific journal)
Joint
A Proposal of Memory and Prediction Based Genetic Algorithm Using Speciation in Dynamic Multimodal Function Optimization
Takumi Ichimura, Hiroshi Inoue, Akira Hara, Tetsuyuki Takahama and Kenneth J. Mackin
Journal of Advanced Computational Intelligence and Intelligent Informatics

15/ 8, 1082-1094
2011/10




37 Research paper (international conference proceedings)
Joint
Cartesian Ant Programming
Akira Hara, Manabu Watanabe, Tetsuyuki Takahama
Proc. of 2011 IEEE International Conference on Systems, Man, and Cybernetics

3161-3166
2011/10




38 Research paper (international conference proceedings)
Joint
Fuzzy C-Means Clustering and Partition Entropy for Species-Best Strategy and Search Mode Selection in Nonlinear Optimization by Differential Evolution
Tetsuyuki Takahama and Setsuko Sakai
Proceedings of 2011 IEEE International Conference on Fuzzy Systems

290-297
2011/06




39 Research paper (international conference proceedings)
Joint
Efficient Nonlinear Optimization by Differential Evolution with a Rotation-Invariant Local Sampling Operation
Tetsuyuki Takahama and Setsuko Sakai
Proc. of 2011 IEEE Congress on Evolutionary Computation

2215-2222
2011/06



Differential Evolution (DE) is a newly proposed evolutionary algorithm. DE has been successfully applied to
optimization problems including non-linear, non-differentiable,
non-convex and multimodal functions. However, the performance of DE degrades in problems having strong dependence among variables, where variables are strongly related to each other.
One of the desirable properties of optimization algorithms for solving the problems with the strong dependence is rotation-invariant property. In DE, the mutation operation is rotation-invariant,
but the crossover operation is not rotation-invariant
usually. In this study, we propose a new operation, called local
sampling operation that is rotation-invariant. In the operation,
independent points are selected from the population, difference
vectors from a parent to the points span a local area centered
at the parent, and a new point is generated around the area.
Also, the operation is used for judging whether intensive search
or extensive search is desirable in each generation. The effect
of the proposed method is shown by solving some benchmark
problems.
40 Research paper (international conference proceedings)
Joint
Memory and Prediction Based Genetic Algorithm Using Speciation in Dynamic Multimodal Function Optimization
Takumi Ichimura, Hiroshi Inoue, Akira Hara, Tetsuyuki Takahama
Proceedings of Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems (SCIS&ISIS 2010)

1335-1340
2010/12




41 Research paper (international conference proceedings)
Joint
Solving Nonlinear Optimization Problems by Differential Evolution with a Rotation-Invariant Crossover Operation using Gram-Schmidt process
Tetsuyuki Takahama and Setsuko Sakai
Proceedings of the World Congress on Nature and Biologically Inspired Computing (NaBIC2010)

533-540
2010/12




42 Research paper (international conference proceedings)
Joint
Multi-Colony Max-Min Ant System Blocking Edges in Local Optima
Akira Hara, Takumi Ichimura, Hirotaka Seo, Tetsuyuki Takahama
Proceedings of Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems (SCIS&ISIS 2010)

1322-1328
2010/12




43 Research paper (international conference proceedings)
Joint
Cartesian genetic programming with reusable genes
Akira Hara, Kotaro Muraoka, Takumi Ichimura, Tetsuyuki Takahama
Proceedings of The 14th Asia Pacific Symposium on Intelligent and Evolutionary Systems

69-78
2010/11




44 Research paper (international conference proceedings)
Joint
Effective Diversification of Ant-Based Search by Considering Agent Traffic in Edges
Akira Hara, Souichi Tanabe, Takumi Ichimura, Tetsuyuki Takahama
Proceedings of SICE Annual Conference 2010

684-689
2010/08




45 Research paper (international conference proceedings)
Joint
Efficient Constrained Optimization by the Epsilon Constrained Adaptive Differential Evolution
Tetsuyuki Takahama and Setsuko Sakai
Proc. of 2010 IEEE Congress on Evolutionary Computation

2052-2059
2010/07



The ε constrained method is an algorithm transformation method, which can convert algorithms for unconstrained problems to algorithms for constrained problems using
the ε level comparison, which compares search points based on the pair of objective value and constraint violation of them. We have proposed the ε constrained differential evolution εDE, which is the combination of the ε constrained method and differential evolution (DE), and have shown that the εDE can run very fast and can find very high quality solutions. In this study, we propose the ε constrained adaptive DE (εADE), which adopts a new and stable way of controlling the ε level and adaptive control of algorithm parameters in DE. The εADE is very efficient constrained optimization algorithm that can find high-quality solutions in very small number of function
evaluations. It is shown that the εADE can find near optimal
solutions stably in about half the number of function evaluations compared with various other methods on well known nonlinear constrained problems.
46 Research paper (international conference proceedings)
Joint
Constrained Optimization by the Epsilon Constrained Differential Evolution with an Archive and Gradient-Based Mutation
Tetsuyuki Takahama and Setsuko Sakai
Proc. of 2010 IEEE Congress on Evolutionary Computation

1680-1688
2010/07




47 Research paper (international conference proceedings)
Joint
Ant Colony Optimization Using Exploratory Ants for Constructing Partial Solutions
Akira Hara, Syuhei Matsushima, Takumi Ichimura and Tetsuyuki Takahama
Proc. of 2010 IEEE Congress on Evolutionary Computation

1140-1146
2010/07




48 Research paper (scientific journal)
Joint
Predicting Stock Price Using Neural Networks Optimized by Differential Evolution with Degeneration
Tetsuyuki Takahama, Setsuko Sakai, Akira Hara, Noriyuki Iwane
Internatinal Journal of Innovative Computing, Information and Control

5/ 12, 5021-5031
2009/12




49 Research paper (scientific journal)
Joint
種分化を導入したDifferential Evolutionによる複数解をもつ多峰性関数の最適化
柴坂美祐喜, 原 章, 市村 匠, 高濱徹行
電子情報通信学会論文誌

J92-D/ 7, 1003-1014
2009/07




50 Research paper (international conference proceedings)
Joint
A Comparative Study on Kernel Smoothers in Differential Evolution with Estimated Comparison Method for Reducing Function Evaluations
Tetsuyuki Takahama and Setsuko Sakai
Proc. of 2009 IEEE Congress on Evolutionary Computation

1367-1374
2009/05




51 Research paper (scientific journal)
Joint
Fast and Stable Constrained Optimization by the epsilon Constrained Differential Evolution
Tetsuyuki Takahama, Setsuko Sakai
Pacific Journal of Optimization

5/ 2, 261-282
2009/05




52 Research paper (scientific journal)
Joint
Efficient Constrained Optimization by the epsilon Constrained Differential Evolution Using an Approximation Model with Low Accuracy
Tetsuyuki Takahama and Setsuko Sakai
Journal of the Japanese Society for Artificial Intelligence

24/ 1, 34-45
2009/01




53 Research paper (international conference proceedings)
Joint
Predicting Stock Price using Neural Networks Optimized by Differential Evolution with Degeneration
Tetsuyuki Takahama, Setsuko Sakai, Akita Hara, Noriyuki Iwane
2008 International Symposium on Intelligent Informatics

20-
2008/12




54 Research paper (international conference proceedings)
Joint
Efficient Optimization by Differential Evolution using Rough Approximation Model with Adaptive Control of Error Margin
Tetsuyuki Takahama, Setsuko Sakai
Proc. of the Joint 4th International Conference on Soft Computing and Intelligent Systems and 9th International Symposium on advanced Intelligent Systems (SCIS&ISIS 2008)

1238-1243
2008/09




55 Research paper (international conference proceedings)
Joint
Presentation of Diverse Solutions by Ant Colony Optimization with Tabu Search
Tetsuyuki Takahama, Setsuko Sakai
Proc. of the Joint 4th International Conference on Soft Computing and Intelligent Systems and 9th International Symposium on advanced Intelligent Systems (SCIS&ISIS 2008)

1412-1417
2008/09




56 Research paper (international conference proceedings)
Joint
Reducing Function Evaluations in Differential Evolution using Rough Approximation-Based Comparison
Tetsuyuki Takahama, Setsuko Sakai
Proc. of 2008 IEEE Congress on Evolutionary Computation

2307-2314
2008/06




57 Research paper (scientific journal)
Joint
Reducing the Number of Function Evaluations in Differential Evolution by Estimated Comparison Method using an Approximation Model with Low Accuracy
Tetsuyuki Takahama, Setsuko Sakai, Akira Hara
The Transactions of the Institute of Electronics, Information and Communication Engineers (IEICE)

J91-D/ 5, 1275-1285
2008/05




58 Research paper (international conference proceedings)
Joint
Structural Learning of Neural Networks by Differential Evolution with Degeneration using Mappings
Tetsuyuki Takahama, Setsuko Sakai, Akira Hara, Iwane Noriyuki
Proc. of 2007 IEEE Congress on Evolutionary
Computation

3434-3441
2007/09




59 Research paper (international conference proceedings)
Joint
Species-based Differential Evolution with Switching Search Strategies for Multimodal Function Optimization
Miyuki Shibasaka, Akira Hara, Takumi Ichimura, Tetsuyuki Takahama
Proc. of 2007 IEEE Congress on Evolutionary Computation

1183-1190
2007/09




60 Research paper (scientific journal)
Joint
Ant Colony Optimization Algorithm with Colony Fission and Extinction
Nobuyuki Fujita, Akira Hara, Takumi Ichimura, Tetsuyuki Takahama
The Transactions of the Institute of Electronics, Information and Communication Engineers (IEICE)

J89-D/ 12, 2661-2670
2006/12




61 Research paper (international conference proceedings)
Joint
Solving Nonlinear Constrained Optimization Problems by the ε Constrained Differential Evolution
Tetsuyuki Takahama, Setsuko Sakai, Noriyuki Iwane
Proc. of 2006 IEEE Conference on Systems, Man, and Cybernetics

2322-2327
2006/10




62 Research paper (international conference proceedings)
Joint
A Proposal of Interactive Genetic Network Programming by Fuzzy Structural Modeling
Takumi Ichimura, Kazuya Sakaguchi, Akira Hara, Tetsuyuki Takahama
Proc. of 2006 IEEE Conference on Systems, Man, and Cybernetics

1172-1177
2006/10




63 Research paper (international conference proceedings)
Joint
Constrained Optimization by the epsilon Constrained Differential Evolution with Gradient-Based Mutation and Feasible Elites
Tetsuyuki Takahama, Setsuko Sakai
Proc. of 2006 IEEE Congress on Evolutionary Computation

308-315
2006/07




64 Research paper (international conference proceedings)
Joint
Effective Diversification of Ant-Based Search Using Colony Fission and Extinction
Akira Hara, Takumi Ichimura, Nobuyuki Fujita, Tetsuyuki Takahama
Proc. of 2006 IEEE Congress on Evolutionary Computation

3773-3780
2006/07




65 Research paper (international conference proceedings)
Joint
Solving Constrained Optimization Problems by the epsilon Constrained Particle Swarm Optimizer with Adaptive Velocity Limit Control
Tetsuyuki Takahama, Setsuko Sakai
Proc. of the 2nd IEEE International Conference on Cybernetics & Intelligent Systems

683-689
2006/06




66 Research paper (scientific journal)
Joint
Constrained Optimization by the epsilon Constrained Genetic Algorithm
Tetsuyuki Takahama, Setsuko Sakai
IPSJ journal

47/ 6, 1861-1871
2006/06




67 Research paper (international conference proceedings)
Joint
Constrained Optimization by the epsilon Constrained Hybrid Algorithm of Particle Swarm Optimization and Genetic Algorithm
Tetsuyuki Takahama, Setsuko Sakai, Noriyuki Iwane
Proc. of the 18th Australian Joint Conference on Artificial Intelligence, Lecture Notes in Computer Science 3809

389-400
2005/12




68 Research paper (scientific journal)
Joint
Constrained Optimization by Applying the alpha Constrained Method to the Nonlinear Simplex Method with Mutations
Tetsuyuki Takahama, Setsuko Sakai
IEEE Transactions on Evolutionary Computation

9/ 5, 437-451
2005/10




69 Research paper (international conference proceedings)
Joint
Extraction of Risk Factors by Multi-agent Voting Model Using Automatically Defined Groups
Akira Hara, Takumi Ichimura, Tetsuyuki Takahama, Yoshinori Isomichi
Proc. of the 9th International Conference on Knowledge-Based Intelligent Information & Engineering Systems (KES2005)

3, 1218-1224
2005/09




70 Research paper (international conference proceedings)
Joint
Effect of Direct Communication in Ant System
Akira Hara, Takumi Ichimura, Tetsuyuki Takahama, Yoshinori Isomichi, Motoki Shigemi
Proc. of the 9th International Conference on Knowledge-Based Intelligent Information & Engineering Systems (KES2005)

1, 925-931
2005/09




71 Research paper (international conference proceedings)
Joint
Constrained Optimization by epsilon Constrained Particle Swarm Optimizer with ε-level Control
Tetsuyuki Takahama, Setsuko Sakai
Proc. of the 4th IEEE International Workshop on Soft Computing as Transdisciplinary Science and Technology (WSTST'05)

1019-1029
2005/05




72 Research paper (scientific journal)
Joint
Constrained Optimization by the alpha Constrained Particle Swarm Optimizer
Tetsuyuki Takahama, Setsuko Sakai
Journal of Advanced Computational Intelligence and Intelligent Informatics

9/ 3, 282-289
2005/05




73 Research paper (international conference proceedings)
Joint
Structural Learning of Neural Networks by Coevolutionary Genetic Algorithm with Degeneration
Tetsuyuki Takahama, Setsuko Sakai
Proc. of 2004 IEEE International Conference on Systems, Man, and Cybernetics (SMC2004)

3507-3512
2004/10




74 Research paper (international conference proceedings)
Joint
Constrained Optimization by Combining the alpha Constrained Method with Particle Swarm Optimization
Tetsuyuki Takahama, Setsuko Sakai
Proc. of Joint 2nd International Conference on Soft Computing and Intelligent Systems and 5th International Symposium on Advanced Intelligent Systems (SCIS&ISIS2004)


2004/09




75 Research paper (international conference proceedings)
Joint
Extraction of Diagnostic Rules from Coronary Heart Disease Database Using Automatically Defined Groups
Akira Hara, Takumi Ichimura, Tetsuyuki Takahama, Yoshinori Isomichi
Proc. of the 8th International Conference on Knowledge-Based Intelligent Information & Engineering Systems (KES2004)

2, 1089-1096
2004/09




76 Research paper (scientific journal)
Joint
Constrained Optimization by alpha Constrained Genetic Algorithm (alphaGA)
Tetsuyuki Takahama, Setsuko Sakai
Systems and Computers in Japan

35/ 5, 11-22
2004/05




77 Research paper (scientific journal)
Joint
Structural Optimization by Genetic Algorithm with Degeneration (GAd)
Tetsuyuki Takahama, Setsuko Sakai, Takumi Ichimura, Yoshinori Isomichi
Systems and Computers in Japan

35/ 5, 32-43
2004/05




78 Research paper (scientific journal)
Joint
A Study on Adaptive Control of Degeneration Speed in Structural Learning of Fuzzy Rules by Genetic Algorithms with Degeneration GAd
T.Takahama, S.Sakai, Y.Isomichi
Journal of Japan Society for Fuzzy Theory and Intelligent Informatics

16/ 1, 33-43
2004/02




79 Research paper (international conference proceedings)
Joint
Structural Learning of RBF-Fuzzy Rule Bases Based on Information Criteria and Degeneration
T.Takahama, S.Sakai, N.Iwane
Proc. of 2003 IEEE International Conference on Systems, Man, and Cybernetics

2581-2586
2003/10




80 Research paper (international conference proceedings)
Joint
Extraction of Rules by Heterogeneous Agents Using Automatically Defined Groups
A.Hara, T.Ichimura, T.Takahama, Y.Isomichi
Proc. of the 7th International Conference on Knowledge-Based Intelligent Information & Engineering Systems (KES2003)

2, 1405-1411
2003/09




81 Research paper (international conference proceedings)
Joint
Multi-Objective Decision Making by AHP and its Application to Personal Preference Retrieval System
T.Ichimrua, A.Hara, T.Takahama, Y.Isomichi, R.Utunomiya
Proc. of the 7th International Conference on Knowledge-Based Intelligent Information & Engineering Systems (KES2003)

2, 474-480
2003/09




82 Research paper (scientific journal)
Joint
Learning Fuzzy Control Rules by α-constrained Simplex Method
T.Takahama, S.Sakai
Systems and Computers in Japan

34/ 6, 80-90
2003/06




83 Research paper (scientific journal)
Joint
Constrained optimization by alpha constrained genetic algorithm (alphaGA)
T.Takahama, S.Sakai
The Transactions of the Institute of Electronics, Information and Communication Engineers (IEICE)

J86-D-I/ 4, 198-207
2003/04




84 Research paper (international conference proceedings)
Joint
Learning Structure of RBF-Fuzzy Rule Bases by Degeneration
T.Takahama, S.Sakai
Proc. of 2003 International Conference on Fuzzy Information Processing

2, 611-616
2003/03




85 Research paper (scientific journal)
Joint
Structural Optimization by Genetic Algorithm with Degeneration (GAd)
T.Takahama, S.Sakai, T.Ichimura, Y.Isomichi
The Transactions of the Institute of Electronics, Information and Communication Engineers (IEICE)

J86-D-I/ 3, 140-149
2003/03




86 Research paper (scientific journal)
Joint
MGGA: Genetic Algorithm with Mutant Genes
T.Takahama, S.Sakai, Y.Isomichi
Systems and Computers in Japan

33/ 14, 23-33
2002/12




87 Research paper (international conference proceedings)
Joint
Structural Optimization of Neural Network by Genetic Algorithm with Damaged Genes
T.Takahama, S.Sakai
Proc. of the 9th International Conference on Neural Information Processing

3, 1211-1215
2002/11




88 Research paper (international conference proceedings)
Joint
Structural Learning by Genetic Algorithm with Damaged Genes
T.Takahama, S.Sakai
Proc. of the IASTED International Conference on Artificial and Computational Intelligence

161-166
2002/09




89 Research paper (scientific journal)
Joint
A Synthesis of Structural Adaptive Learning Algorithm in Neural Network Based on the Theory of Evolution
S.Oeda, T.Ichimura, M.Terauchi, T.Takahama, Y.Isomichi
Information Processing Society of Japan (IPSJ) Journal

43/ 8, 2728-2738
2002/08




90 Research paper (international conference proceedings)
Joint
Learning Game Strategy in an Artificial Game Society
T.Takahama, S.Sakai
Proc. of Pan-Yellow-Sea International Workshop on Information Technologies for Network Era 2002

172-179
2002/03




91 Research paper (international conference proceedings)
Joint
A Proposal of Interactive Tool for Implementing a Sequence of Learning Tasks in a Drill and Practice by Fuzzy Petri Nets and its Application to E-learning System for the National Examination for Medical Practitioners
T.Ichimura, A.Sugihara, T.Takahama, Y.Isomichi, K.Yoshida
Proc. of the 2nd Vietnam-Japan Bilateral Symposium on Fuzzy Systems and Applications (VJFUZZY'2001)

210-217
2001/12




92 Research paper (scientific journal)
Joint
Interactive Learning of Multiobjective Fuzzy Control Rules by Multiobjective Nonlinear Optimization Method "Vector Simplex"
T.Takahama, S.Sakai
Information Processing Society of Japan (IPSJ) Journal

42/ 11, 2607-2617
2001/11




93 Research paper (international conference proceedings)
Joint
Extraction of emotion from facial expression by parallel sand glass type neural networks
T.Ichimura, H.Ishida, M.Terauchi, T.Takahama, Y.Isomichi
Proc. of the 5th International Conference on Knowledge-Based Intelligent Engineering Systems & Allied Technologies (KES2001)

1, 988-992
2001/09




94 Research paper (scientific journal)
Joint
MGGA: Genetic Algorithm with Mutant Genes
T.Takahama, S.Sakai, Y.Isomichi
The Transactions of the Institute of Electronics, Information and Communication Engineers (IEICE)

J84-D-I/ 9, 1297-1306
2001/09




95 Research paper (international conference proceedings)
Joint
Learning Game Strategy by Multi-Agent TD Players
T.Takahama, S.Sakai
Proc. of 2nd International Conference on Software Engineering, Artificial Intelligence, Networking & Parallel/Distributed Computing

731-738
2001/08




96 Research paper (international conference proceedings)
Joint
Darwinian Inheritance Genetic Learning Method of Neural Networks Under Dynamic Environment
S.Oeda, T.Ichimura, M.Terauchi, T.Takahama, Y.Isomichi
Proc. of the INNS-IEEE International Joint Conference on Neural Networks (IJCNN2001)

2235-2240
2001/07




97 Research paper (international conference proceedings)
Joint
Learning Fuzzy Control Rules by Vector Simplex Method
S.Sakai, T.Takahama
Proc. of Joint 9th IFSA World Congress and 20th NAFIPS International Conference

5, 2541-2546
2001/07




98 Research paper (international conference proceedings)
Joint
Team Model and Taught-by-the-best Operation
T.Takahama, S.Sakai
Proc. of the Congress on Evolutionary Computation 2001

2, 1093-1100
2001/05




99 Research paper (international conference proceedings)
Joint
Multiobjective Nonlinear Optimization Method "Vector Simplex"
T.Takahama, S.Sakai
Proc. of the International Conference on Parametric Optimization and Related Topics V

179-193
2000




100 Research paper (scientific journal)
Joint
Tuning Fuzzy Control Rules by the Alpha Constrained Method which Solves Constrained Nonlinear Optimization Problems
Tetsuyuki Takahama, Setsuko Sakai
Electronics and Communications in Japan, Part3: Fundamental Electronic Science

83/ 9, 1-12
2000/09




101 Research paper (scientific journal)
Joint
Learning Fuzzy Inference Rules with Shape Parameters
T.Takahama, S.Sakai, T.Ichimura, Y.Isomichi
The Transactions of the Institute of Electronics, Information and Communication Engineers (IEICE)

J83-D-I/ 9, 1025-1029
2000/09




102 Research paper (international conference proceedings)
Joint
Adaptive Evolutional Method of Neural Networks using Genetic Algorithms under Dynamic Environments
S.Oeda,T.Ichimura,M.Terauchi,T.Takahama, Y.Isomichi
Proc. of Fourth International Conference on Knowledge-Based Intelligent Engineering Systems & Allied Technologies (KES'2000)

2, 742-745
2000/08




103 Research paper (scientific journal)
Joint
Learning Fuzzy Control Rules by Alpha Constrained Simplex Method
T.Takahama, S.Sakai
The Transactions of the Institute of Electronics, Information and Communication Engineers (IEICE)

J83-D-I/ 7, 770-779
2000/07




104 Research paper (scientific journal)
Joint
Team Model which Solves Optimization Problems based on Teaching by Teams
T.Takahama, S.Sakai, Y.Isomichi
The Transactions of the Institute of Electronics, Information and Communication Engineers (IEICE)

J83-D-I/ 1, 143-152
2000/01




105 Research paper (international conference proceedings)
Joint
An optimization Algorithm "Team Model"
T.Takahama, S.Sakai
Proceedings of 1999 IEEE International Conference on Systems, Man, and Cybernetics

3, 563-568
1999/10




106 Research paper (international conference proceedings)
Joint
Learning Fuzzy Control Rules by Alpha Constrained Simplex Method
S.Sakai, T.Takahama
Proceedings of the 4th Asian/Pacific International Symposium on Instrumentation, Measurement & Automatic Control

240-246
1999/08




107 Research paper (international conference proceedings)
Joint
Learning Fuzzy Control Rules by Alpha Constrained Powell's Method
T.Takahama, S.Sakai
Proceedings of 1999 IEEE International Fuzzy Systems Conference

650-655
1999/08




108 Research paper (scientific journal)
Joint
Tuning fuzzy control rules by alpha constrained method which solves constrained nonlinear optimization problems
T.Takahama, S.Sakai
The Transactions of the Institute of Electronics, Information and Communication Engineers (IEICE)

J82-A/ 5, 658-668
1999/05




109 Research paper (scientific journal)
Joint
Learning Rules for Fuzzy Scaling Control by Genetic Algorithms
T.Takahama, S.Sakai
The Transactions of the Institute of Electronics, Information and Communication Engineers (IEICE)

J81-D-II/ 1, 119-126
1998/01




110 Research paper (scientific journal)
Joint
A Method of Separating the Graphical User Interface in CAI System
T.Takahama, T.Makino, S.Sakai
Transactions of Japanese Society for Information and Systems in Education

13/ 4, 199-212
1997/01




111 Research paper (scientific journal)
Joint
倒立2重振子系に対するファジイ制御知識の表現とスケーリングによる適応制御
R.Muchamad, T.Takahama, T.Odaka, H.Ogura
日本ファジイ学会誌

8/ 3, 576-585
1996/06




112 Research paper (international conference proceedings)
Joint
Learning Fuzzy Rules for Bang-Bang Control by Reinforcement Learning Method
T.Takahama, S.Sakai
Proceedings of 1996 International Fuzzy Systems and Intelligent Conference

193-202
1996/04




113 Research paper (scientific journal)
Joint
Learning Fuzzy Rules for Bang-Bang Control by Reinforcement Learning Method
T.Takahama, S.Sakai, H.Ogura, M.Nakamura
Journal of Japan Society for Fuzzy Theory and Systems

8/ 1, 115-122
1996/02




114 Research paper (scientific journal)
Joint
XTSS: Graphical User Interface System for Integrating Text Based Applications on X Window
T.Takahama, H.Ogura, M.Nakamura
The Transactions of the Institute of Electronics, Information and Communication Engineers (IEICE)

J77-D-I/ 7, 493-502
1994/07




115 Research paper (scientific journal)
Joint
A Visual Data Analysis System for the Medical Image Processing
T.Odaka, T.Takahama, H.Wagatsuma, K.Shimada, H.Ogura
Journal of Medical Systems

18/ 3, 167-173
1994/06




116 Research paper (scientific journal)
Joint
Knowledge Representation Schema in an Interactive CAI System for Junior English Course
H.Ogura, Y.Uomi, T.Takahama, T.Odaka
Journal of Japan Society for CAI

11/ 1, 3-13
1994/04




117 Research paper (scientific journal)
Joint
User Interface System for Application Integration on X Window (UAI/X)
T.Takahama, Y.Nakaya, H.Ogura, M.Nakamura
The Transactions of the Institute of Electronics, Information and Communication Engineers (IEICE)

J75-D-I/ 7, 479-487
1992/07