Success-history based parameter adaptation for Differential Evolution. - Ryoji Tanabe; Alex Fukunaga (IEEE Congress on Evolutionary Computation, 2013)

Evaluation of a randomized parameter setting strategy for island-model evolutionary algorithms. - Ryoji Tanabe; Alex Fukunaga (IEEE Congress on Evolutionary Computation, 2013)

Evaluating the performance of SHADE on CEC 2013 benchmark problems. - Ryoji Tanabe; Alex Fukunaga (IEEE Congress on Evolutionary Computation, 2013)

Improving the search performance of SHADE using linear population size reduction. - Ryoji Tanabe; Alex S. Fukunaga (IEEE Congress on Evolutionary Computation, 2014)

On the pathological behavior of adaptive differential evolution on hybrid objective functions. - Ryoji Tanabe; Alex S. Fukunaga (GECCO, 2014)

Reevaluating Exponential Crossover in Differential Evolution. - Ryoji Tanabe; Alex Fukunaga (PPSN, 2014)

Optimization of oil reservoir models using tuned evolutionary algorithms and adaptive differential evolution. - Claus Aranha; Ryoji Tanabe; Romain Louis Chassagne; Alex S. Fukunaga (CEC, 2015)

Tuning differential evolution for cheap, medium, and expensive computational budgets. - Ryoji Tanabe; Alex Fukunaga (CEC, 2015)

A Note on Multi-Funnel Functions for Expensive Optimization Scenario. - Ryoji Tanabe (GECCO (Companion), 2015)

How Far Are We from an Optimal, Adaptive DE? - Ryoji Tanabe; Alex Fukunaga (PPSN, 2016)

A note on constrained multi-objective optimization benchmark problems. - Ryoji Tanabe; Akira Oyama (CEC, 2017)

The Impact of Population Size, Number of Children, and Number of Reference Points on the Performance of NSGA-III. - Ryoji Tanabe; Akira Oyama (EMO, 2017)

TPAM: a simulation-based model for quantitatively analyzing parameter adaptation methods. - Ryoji Tanabe; Alex Fukunaga (GECCO, 2017)

Benchmarking MOEAs for multi- and many-objective optimization using an unbounded external archive. - Ryoji Tanabe; Akira Oyama (GECCO, 2017)

Benchmarking Multi- and Many-Objective Evolutionary Algorithms Under Two Optimization Scenarios. - Ryoji Tanabe; Hisao Ishibuchi; Akira Oyama (IEEE Access, 2017)

A Decomposition-Based Evolutionary Algorithm for Multi-modal Multi-objective Optimization. - Ryoji Tanabe; Hisao Ishibuchi (PPSN (1), 2018)

An analysis of control parameters of MOEA/D under two different optimization scenarios. - Ryoji Tanabe; Hisao Ishibuchi (Appl. Soft Comput., 2018)

Non-elitist evolutionary multi-objective optimizers revisited. - Ryoji Tanabe; Hisao Ishibuchi (GECCO, 2019)

Review and analysis of three components of the differential evolution mutation operator in MOEA/D-DE. - Ryoji Tanabe; Hisao Ishibuchi (Soft Comput., 2019)

A niching indicator-based multi-modal many-objective optimizer. - Ryoji Tanabe; Hisao Ishibuchi (Swarm Evol. Comput., 2019)

Analyzing adaptive parameter landscapes in parameter adaptation methods for differential evolution. - Ryoji Tanabe (GECCO, 2020)

Revisiting Population Models in Differential Evolution on a Limited Budget of Evaluations. - Ryoji Tanabe (PPSN (1), 2020)

An easy-to-use real-world multi-objective optimization problem suite. - Ryoji Tanabe; Hisao Ishibuchi (Appl. Soft Comput., 2020)

An Analysis of Quality Indicators Using Approximated Optimal Distributions in a Three-dimensional Objective Space. - Ryoji Tanabe; Hisao Ishibuchi (CoRR, 2020)

A Review of Evolutionary Multi-modal Multi-objective Optimization. - Ryoji Tanabe; Hisao Ishibuchi (CoRR, 2020)

A Framework to Handle Multi-modal Multi-objective Optimization in Decomposition-based Evolutionary Algorithms. - Ryoji Tanabe; Hisao Ishibuchi (CoRR, 2020)

Reviewing and Benchmarking Parameter Control Methods in Differential Evolution. - Ryoji Tanabe; Alex Fukunaga (IEEE Trans. Cybern., 2020)

A Review of Evolutionary Multimodal Multiobjective Optimization. - Ryoji Tanabe; Hisao Ishibuchi (IEEE Trans. Evol. Comput., 2020)

A Framework to Handle Multimodal Multiobjective Optimization in Decomposition-Based Evolutionary Algorithms. - Ryoji Tanabe; Hisao Ishibuchi (IEEE Trans. Evol. Comput., 2020)

An Analysis of Quality Indicators Using Approximated Optimal Distributions in a 3-D Objective Space. - Ryoji Tanabe; Hisao Ishibuchi (IEEE Trans. Evol. Comput., 2020)

Towards exploratory landscape analysis for large-scale optimization: a dimensionality reduction framework. - Ryoji Tanabe (GECCO, 2021)

Benchmarking the hooke-jeeves method, MTS-LS1, and BSrr on the large-scale BBOB function set. - Ryoji Tanabe (GECCO Companion, 2022)

A two-phase framework with a bézier simplex-based interpolation method for computationally expensive multi-objective optimization. - Ryoji Tanabe; Youhei Akimoto; Ken Kobayashi; Hiroshi Umeki; Shinichi Shirakawa; Naoki Hamada (GECCO, 2022)

Benchmarking Feature-Based Algorithm Selection Systems for Black-Box Numerical Optimization. - Ryoji Tanabe (IEEE Trans. Evol. Comput., 2022)

On the Unbounded External Archive and Population Size in Preference-based Evolutionary Multi-objective Optimization Using a Reference Point. - Ryoji Tanabe (GECCO, 2023)

Quality Indicators for Preference-based Evolutionary Multi-objective Optimization Using a Reference Point: A Review and Analysis. - Ryoji Tanabe; Ke Li 001 (CoRR, 2023)

Benchmarking Parameter Control Methods in Differential Evolution for Mixed-Integer Black-Box Optimization. - Ryoji Tanabe (GECCO, 2024)

On Constructing Algorithm Portfolios in Algorithm Selection for Computationally Expensive Black-box Optimization in the Fixed-budget Setting. - Takushi Yoshikawa; Ryoji Tanabe (GECCO Companion, 2024)

Contrasting the Landscapes of Feature Selection Under Different Machine Learning Models. - Arnaud Liefooghe; Ryoji Tanabe; Sébastien Vérel (PPSN (1), 2024)

Investigating normalization in preference-based evolutionary multi-objective optimization using a reference point. - Ryoji Tanabe (Appl. Soft Comput., 2024)

Quality Indicators for Preference-Based Evolutionary Multiobjective Optimization Using a Reference Point: A Review and Analysis. - Ryoji Tanabe; Ke Li 001 (IEEE Trans. Evol. Comput., 2024)

Analyzing the Landscape of the Indicator-based Subset Selection Problem. - Keisuke Korogi; Ryoji Tanabe (GECCO, 2025)

Speeding up Local Search for the Indicator-based Subset Selection Problem by a Candidate List Strategy. - Keisuke Korogi; Ryoji Tanabe (CoRR, 2025)