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2020 – today
- 2022
[j11]Tetsuyuki Takahama, Setsuko Sakai:
Multimodal optimization by particle swarm optimization with graph-based speciation using β-relaxed relative neighborhood graph and seed-centered mutation. Artif. Life Robotics 27(2): 236-247 (2022)- [c30]Tetsuyuki Takahama, Setsuko Sakai:
Converging and Moving Detection Using an Outward Vector Rate for Adaptive Differential Evolution. SCIS/ISIS 2022: 1-8 - 2021
- [c29]Tetsuyuki Takahama, Setsuko Sakai:
An Adaptive Differential Evolution Algorithm Utilizing Failure Information and Success Information. CEC 2021: 17-24 - 2020
- [j10]Tetsuyuki Takahama, Setsuko Sakai:
Constrained optimization by improved particle swarm optimization with the equivalent penalty coefficient method. Artif. Life Robotics 25(4): 612-623 (2020) - [c28]Tetsuyuki Takahama, Setsuko Sakai:
Adaptive Directional Mutation for an Adaptive Differential Evolution Algorithm. SCIS/ISIS 2020: 1-7
2010 – 2019
- 2019
- [c27]Tetsuyuki Takahama, Setsuko Sakai:
An Equivalent Penalty Coefficient Method: An Adaptive Penalty Approach for Population-Based Constrained Optimization. CEC 2019: 1620-1627 - 2018
- [j9]Tetsuyuki Takahama, Setsuko Sakai:
The velocity updating rule according to an oblique coordinate system with mutation and dynamic scaling for particle swarm optimization. Artif. Life Robotics 23(4): 618-627 (2018) - [c26]Tetsuyuki Takahama, Setsuko Sakai:
A New Binomial Crossover Considering Correlation Among Decision Variables for Adaptive Differential Evolution. SCIS&ISIS 2018: 467-473 - 2017
- [c25]Tetsuyuki Takahama, Setsuko Sakai:
An Adaptive Differential Evolution with Learning Parameters According to Groups Defined by the Rank of Objective Values. ICSI (1) 2017: 411-419 - 2016
- [j8]Tetsuyuki Takahama, Setsuko Sakai, Jun-ichi Kushida, Akira Hara:
Particle swarm optimization with mutation operations controlled by landscape modality estimation using hill-valley detection. Artif. Life Robotics 21(4): 423-433 (2016) - [j7]Tetsuyuki Takahama, Setsuko Sakai:
Improving an adaptive differential evolution using hill-valley detection. Int. J. Hybrid Intell. Syst. 13(1): 1-13 (2016) - [c24]Tetsuyuki Takahama, Setsuko Sakai:
An adaptive differential evolution with adaptive archive selection and hill-valley detection. CEC 2016: 48-55 - 2015
- [c23]Tetsuyuki Takahama, Setsuko Sakai:
Emerging collective intelligence in Othello players evolved by differential evolution. CIG 2015: 214-221 - [c22]Tetsuyuki Takahama, Setsuko Sakai:
Improving an adaptive differential evolution using hill-valley detection. SoCPaR 2015: 284-289 - 2014
- [c21]Tetsuyuki Takahama, Setsuko Sakai:
An adaptive differential evolution considering correlation of two algorithm parameters. SCIS&ISIS 2014: 618-623 - 2013
- [c20]Tetsuyuki Takahama, Setsuko Sakai:
Efficient constrained optimization by the ε constrained differential evolution with rough approximation using kernel regression. IEEE Congress on Evolutionary Computation 2013: 1334-1341 - 2012
- [c19]Tetsuyuki Takahama, Setsuko Sakai:
Large scale optimization by differential evolution with landscape modality detection and a diversity archive. IEEE Congress on Evolutionary Computation 2012: 1-8 - [c18]Tetsuyuki Takahama, Setsuko Sakai:
Efficient Constrained Optimization by the ε Constrained Rank-Based Differential Evolution. IEEE Congress on Evolutionary Computation 2012: 1-8 - [c17]Tetsuyuki Takahama, Setsuko Sakai:
Differential evolution with dynamic strategy and parameter selection by detecting landscape modality. IEEE Congress on Evolutionary Computation 2012: 1-8 - [c16]Tetsuyuki Takahama, Setsuko Sakai:
Differential Evolution with Graph-Based Speciation by Competitive Hebbian Rules. ICGEC 2012: 445-448 - 2011
- [c15]Tetsuyuki Takahama, Setsuko Sakai:
Efficient nonlinear optimization by differential evolution with a rotation-invariant local sampling operation. IEEE Congress on Evolutionary Computation 2011: 2215-2222 - [c14]Tetsuyuki Takahama, Setsuko Sakai:
Fuzzy c-means clustering and partition entropy for species-best strategy and search mode selection in nonlinear optimization by differential evolution. FUZZ-IEEE 2011: 290-297 - 2010
- [c13]Tetsuyuki Takahama, Setsuko Sakai:
Constrained optimization by the epsilon constrained differential evolution with an archive and gradient-based mutation. IEEE Congress on Evolutionary Computation 2010: 1-9 - [c12]Tetsuyuki Takahama, Setsuko Sakai:
Efficient constrained optimization by the epsilon constrained adaptive differential evolution. IEEE Congress on Evolutionary Computation 2010: 1-8 - [c11]Tetsuyuki Takahama, Setsuko Sakai:
Solving nonlinear optimization problems by Differential Evolution with a rotation-invariant crossover operation using Gram-Schmidt process. NaBIC 2010: 526-533
2000 – 2009
- 2009
- [c10]Tetsuyuki Takahama, Setsuko Sakai:
A comparative study on kernel smoothers in Differential Evolution with estimated comparison method for reducing function evaluations. IEEE Congress on Evolutionary Computation 2009: 1367-1374 - 2008
- [c9]Tetsuyuki Takahama, Setsuko Sakai:
Reducing function evaluations in Differential Evolution using rough approximation-based comparison. IEEE Congress on Evolutionary Computation 2008: 2307-2314 - 2007
- [c8]Tetsuyuki Takahama, Setsuko Sakai, Akira Hara, Noriyuki Iwane:
Structural learning of neural networks by differential evolution with degeneration using mappings. IEEE Congress on Evolutionary Computation 2007: 3434-3441 - 2006
- [c7]Tetsuyuki Takahama, Setsuko Sakai:
Constrained Optimization by the ε Constrained Differential Evolution with Gradient-Based Mutation and Feasible Elites. IEEE Congress on Evolutionary Computation 2006: 1-8 - [c6]Tetsuyuki Takahama, Setsuko Sakai, Noriyuki Iwane:
Solving Nonlinear Constrained Optimization Problems by the ε Constrained Differential Evolution. SMC 2006: 2322-2327 - 2005
- [j6]Tetsuyuki Takahama, Setsuko Sakai:
Constrained Optimization by the alpha Constrained Particle Swarm Optimizer. J. Adv. Comput. Intell. Intell. Informatics 9(3): 282-289 (2005) - [j5]Tetsuyuki Takahama, Setsuko Sakai:
Constrained optimization by applying the α constrained method to the nonlinear simplex method with mutations. IEEE Trans. Evol. Comput. 9(5): 437-451 (2005) - [c5]Tetsuyuki Takahama, Setsuko Sakai, Noriyuki Iwane:
Constrained Optimization by the epsilon Constrained Hybrid Algorithm of Particle Swarm Optimization and Genetic Algorithm. Australian Conference on Artificial Intelligence 2005: 389-400 - [c4]Tetsuyuki Takahama, Setsuko Sakai:
Constrained Optimization by epsilon Constrained Particle Swarm Optimizer with epsilon-level Control. WSTST 2005: 1019-1029 - 2004
- [j4]Tetsuyuki Takahama, Setsuko Sakai:
Constrained optimization by constrained genetic algorithm (alpha GA). Syst. Comput. Jpn. 35(5): 11-22 (2004) - [j3]Tetsuyuki Takahama, Setsuko Sakai, Takumi Ichimura, Yoshinori Isomichi:
Structural optimization by genetic algorithm with degeneration (GA d). Syst. Comput. Jpn. 35(5): 32-43 (2004) - [c3]Tetsuyuki Takahama, Setsuko Sakai:
Structural learning of neural networks by coevolutionary genetic algorithm with degeneration. SMC (4) 2004: 3507-3512 - 2003
- [j2]Tetsuyuki Takahama, Setsuko Sakai:
Learning fuzzy control rules by alpha-constrained Simplex method. Syst. Comput. Jpn. 34(6): 80-90 (2003) - [c2]Tetsuyuki Takahama, Setsuko Sakai, Noriyuki Iwane:
Structural learning of RBF-fuzzy rule bases based on information criteria and degeneration. SMC 2003: 2581-2586 - 2002
- [j1]Tetsuyuki Takahama, Setsuko Sakai, Yoshinori Isomichi:
MGGA: Genetic algorithm with mutant genes. Syst. Comput. Jpn. 33(14): 23-33 (2002) - 2001
- [c1]Tetsuyuki Takahama, Setsuko Sakai:
Team model and taught-by-the-best operation. CEC 2001: 1093-1100
Coauthor Index
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