Výsledky vyhledávání - "Tetsuyuki Takahama"

An Adaptive Differential Evolution Algorithm Utilizing Failure Information and Success Information

Autor: Tetsuyuki Takahama, Setsuko Sakai

Publikováno v: CEC

Differential Evolution (DE) has been successfully applied to a variety of optimization problems. The performance of DE is affected by two algorithm parameters of the scaling factor and the crossover rate. Much research has been done in order to adapt

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::39852831700d39bcda1272c05ef2b288
https://doi.org/10.1109/cec45853.2021.9504715

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Efficient Data Presentation Method for Building User Preference Model Using Interactive Evolutionary Computation

Autor: Jun-ichi Kushida, Tetsuyuki Takahama, Akira Hara, Ryohei Yasuda

Publikováno v: Intelligent Decision Technologies ISBN: 9789811627644
KES-IDT

In this paper, we propose data presentation methods for building user preference models efficiently in recommendation systems by interactive genetic algorithm. The user preference model of the recommender agent is represented by a three-layer neural

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::43a31e9063244c72cdac7cd58354612e
https://doi.org/10.1007/978-981-16-2765-1_48

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Adaptive Directional Mutation for an Adaptive Differential Evolution Algorithm

Autor: Setsuko Sakai, Tetsuyuki Takahama

Publikováno v: SCIS/ISIS

Differential Evolution (DE) has been successfully applied to various optimization problems. The performance of DE is affected by algorithm parameters, mutation strategies and so on. One of the most successful studies on controlling the parameters is

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::dc2ae3c152ee1a09328b573251b96be6
https://doi.org/10.1109/scisisis50064.2020.9322724

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Maintaining Population Diversity in Deterministic Geometric Semantic Genetic Programming by ϵ-Lexicase Selection

Autor: Akira Hara, Tetsuyuki Takahama, Jun-ichi Kushida

Publikováno v: SMC

Genetic Programming (GP) is an evolutionary method for automatic programming. In recent years, crossover operators based on the semantics of programs have attracted much attention for improving the search efficiency. We have previously proposed a sem

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::06fe65b7934adf4b0cff4c4cef717531
https://doi.org/10.1109/smc42975.2020.9283096

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The velocity updating rule according to an oblique coordinate system with mutation and dynamic scaling for particle swarm optimization

Autor: Setsuko Sakai, Tetsuyuki Takahama

Publikováno v: Artificial Life and Robotics. 23:618-627

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 u

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::7ecdace07d50024081c4615c8028fc50
https://doi.org/10.1007/s10015-018-0498-y

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Efficient Constraint Handling based on the Adaptive Penalty Method with Balancing the Objective Function Value and the Constraint Violation

Autor: Tetsuyuki Takahama, Akira Hara, Takeshi Kawachi, Jun-ichi Kushida

Publikováno v: IWCIA

Real world problems are often formularized as constrained optimization problems (COPs). Constraint handling techniques are important for efficient search, and various approaches such as penalty methods or feasibility rules have been studied. The pena

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::38171adb1f0563e004dad08bbb5a4b0f
https://doi.org/10.1109/iwcia47330.2019.8955094

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Time Series Prediction Using Deterministic Geometric Semantic Genetic Programming

Autor: Akira Hara, Jun-ichi Kushida, Tetsuyuki Takahama

Publikováno v: SMC

Predicting time series data is one of the most important challenges in many different application domains. Constructing the prediction models can be regarded as symbolic regressions, and the model can be optimized by Genetic Programming (GP), which i

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d94eeafe6a8dcd983cb5051b91df2363
https://doi.org/10.1109/smc.2019.8914562

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L-SHADE with an adaptive penalty method of balancing the objective value and the constraint violation

Autor: Tetsuyuki Takahama, Jun-ichi Kushida, Akira Hara, Takeshi Kawachi

Publikováno v: GECCO (Companion)

In the constraint-handling techniques, the penalty approaches (especially the adaptive penalty methods) are simple and flexible, and have been combined with various Evolutionary Algorithms so far. In this paper, we propose a new adaptive penalty meth

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::18831b9ca266a99847b78d258b90b29a
https://doi.org/10.1145/3319619.3326749

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An Equivalent Penalty Coefficient Method: An Adaptive Penalty Approach for Population-Based Constrained Optimization

Autor: Tetsuyuki Takahama, Setsuko Sakai

Publikováno v: CEC

The penalty function method has been widely used for solving constrained optimization problems. In the method, an extended objective function, which is the sum of the objective value and the constraint violation weighted by the penalty coeffi-cient,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::0ae77e36f35e494d136702e866307a3a
https://doi.org/10.1109/cec.2019.8790360

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