Google Scholar, DBLP
All pdf files on this page are author’s versions, not final published versions.
Preprints
None.
Refereed Journal Papers
2024
- Ryoji Tanabe: Investigating Normalization in Preference-based Evolutionary Multi-objective Optimization Using a Reference Point, Applied Soft Computing, pdf, link
2023
2022
2021
None.
2020
- Ryoji Tanabe and Hisao Ishibuchi: An Analysis of Quality Indicators Using Approximated Optimal Distributions in a Three-dimensional Objective Space, IEEE Transactions on Evolutionary Computation, pdf, supplementary-pdf, supplemental-website, link
- Typos: The explanation of the new R2 indicator (NR2) in Section II-B-6 (page 3) is incorrect. In equation (7),
min should be replaced with max. In contrast, in equation (8), max should be replaced with min.
- Ryoji Tanabe and Hisao Ishibuchi: A Framework to Handle Multi-modal Multi-objective Optimization in Decomposition-based Evolutionary Algorithms, IEEE Transactions on Evolutionary Computation, pdf, supplementary-pdf, code, link
- This paper is an extended version of our PPSN2018 paper
- Ryoji Tanabe and Hisao Ishibuchi: A Review of Evolutionary Multi-modal Multi-objective Optimization, IEEE Transactions on Evolutionary Computation, pdf, code, link
- Ryoji Tanabe and Alex Fukunaga: Reviewing and Benchmarking Parameter Control Methods in Differential Evolution, IEEE Transactions on Cybernetics, link, pdf, supplemental-pdf, supplemental-website
- Ryoji Tanabe and Hisao Ishibuchi: An Easy-to-use Real-world Multi-objective Optimization Problem Suite, Applied Soft Computing, pdf, supplementary-pdf, code, link
2019
2018
2017
- Ryoji Tanabe, Hisao Ishibuchi, and Akira Oyama: Benchmarking Multi- and Many-objective Evolutionary Algorithms under Two Optimization Scenarios, IEEE Access (link-to-pdf), (supplemental-website)
- This paper is an extended version of our GECCO2017 paper
Refereed Conference Papers
2024
- Arnaud Liefooghe, Ryoji Tanabe, and Sébastien Verel: Contrasting the Landscapes of Feature Selection under Different Machine Learning Models, Proc. Parallel Problem Solving from Nature (PPSN2024), pdf, poster
- Ryoji Tanabe: Benchmarking Parameter Control Methods in Differential Evolution for Mixed-Integer Black-Box Optimization, Proc. ACM Genetic and Evolutionary Computation Conference (GECCO2024), pdf, code, ppdata, exdata, slides
2023
- Ryoji Tanabe: On the Unbounded External Archive and Population Size in Preference-based Evolutionary Multi-objective Optimization Using a Reference Point, Proc. ACM Genetic and Evolutionary Computation Conference (GECCO2023), pdf, code, slides
- Nominated for the best paper at the EMO track in GECCO2023
2022
- Ryoji Tanabe, Youhei Akimoto, Ken Kobayashi, Hiroshi Umeki, Shinichi Shirakawa, Naoki Hamada: A Two-phase Framework with a Bezier Simplex-based Interpolation Method for Computationally Expensive Multi-objective Optimization, Proc. ACM Genetic and Evolutionary Computation Conference (GECCO2022), pdf, supplement, code, slides, YouTube
2021
2020
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Ryoji Tanabe: Revisiting Population Models in Differential Evolution on a Limited Budget of Evaluations, Proc. Parallel Problem Solving from Nature (PPSN2020), pdf, code, data, poster
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Ryoji Tanabe: Analyzing Adaptive Parameter Landscapes in Parameter Adaptation Methods for Differential Evolution, Proc. ACM Genetic and Evolutionary Computation Conference (GECCO2020), pdf, supplemental-pdf, code, slide
2019
2018
- Ryoji Tanabe and Hisao Ishibuchi: A Decomposition-based Evolutionary Algorithm for Multi-modal Multi-objective Optimization, Proc. Parallel Problem Solving from Nature (PPSN2018) (pdf), (code)
- Nominated for the best paper at PPSN2018
2017
- Ryoji Tanabe and Alex Fukunaga: TPAM: A Simulation-Based Model for Quantitatively Analyzing Parameter Adaptation Methods, Proc. ACM Genetic and Evolutionary Computation Conference (GECCO2017), (pdf), (supplemental-pdf), (slide)
- Ryoji Tanabe and Akira Oyama: Benchmarking MOEAs for Multi- and Many-objective Optimization Using an Unbounded External Archive, Proc. ACM Genetic and Evolutionary Computation Conference (GECCO2017), (pdf), (supplemental-pdf), (slide)
- Experimental data used in this paper can be downloaded from here
- Ryoji Tanabe and Akira Oyama: A Note on Constrained Multi-Objective Optimization Benchmark Problems, Proc. IEEE Congress on Evolutionary Computation (CEC2017), (pdf)
- The source code of seven (six) real-world problems can be downloaded from here
- Ryoji Tanabe and Akira Oyama: The Impact of Population Size, Number of Children, and Number of Reference Points on The Performance of NSGA-III, Proc. Evolutionary Multi-Criterion Optimization (EMO2017), (pdf)
2016
2015
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Ryoji Tanabe: A Note on Multi-Funnel Functions for Expensive Optimization Scenario, Proc. ACM Genetic and Evolutionary Computation Conference (GECCO2015), two-page poster paper, (pdf)
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Ryoji Tanabe and Alex Fukunaga: Tuning Differential Evolution for Cheap, Medium, and Expensive Computational Budgets, Proc. IEEE Congress on Evolutionary Computation (CEC2015), (pdf)
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Claus de Castro Aranha, Ryoji Tanabe, Romain Chassagne, Alex Fukunaga: Optimization of Oil Reservoir Models Using Tuned Evolutionary Algorithms and Adaptive Differential Evolution, Proc. IEEE Congress on Evolutionary Computation (CEC2015), (pdf)
2014
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Ryoji Tanabe and Alex Fukunaga: Reevaluating Exponential Crossover in Differential Evolution, Proc. Parallel Problem Solving from Nature (PPSN2014), (pdf), (supplemental pdf)
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Ryoji Tanabe and Alex Fukunaga: On the Pathological Behavior of Adaptive Differential Evolution on Hybrid Objective Functions, Proc. ACM Genetic and Evolutionary Computation Conference (GECCO2014), (pdf), (supplemental pdf)
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Ryoji Tanabe and Alex Fukunaga: Improving the Search Performance of SHADE Using Linear Population Size Reduction, Proc. IEEE Congress on Evolutionary Computation (CEC2014), (pdf)
- L-SHADE was a first ranked algorithm at the CEC2014 Competition on Real-Parameter Single Objective Optimization (link).
- Experimental data used in this paper are here
- Originally submitted source code of L-SHADE 1.0.0: (C++ code)
- Please note that this C++ code (also available at the competition website) was submitted to the CEC2014 competition. However, this code had a bug in the archive update mechanism which resulted in a slight performance degradation, but all constraints were satisfied and all solutions found were valid. For better results, please use the corrected version below:
2013
Refereed Workshop Papers
2024
2022
- Ryoji Tanabe: Benchmarking the Hooke-Jeeves Method, MTS-LS1, and BSrr on the Large-scale BBOB Function Set, GECCO Workshop on Black-Box Optimization Benchmarking (BBOB 2022), pdf, code, slides, YouTube