316 citations · 987 across the 20 of their papers we have counts for
20 papers
Speeding up Local Search for the Indicator-based Subset Selection Problem by a Candidate List Strategy
Keisuke Korogi, Ryoji Tanabe
In evolutionary multi-objective optimization, the indicator-based subset selection problem involves finding a subset of points that maximizes a given quality indicator. Local searc…
Investigating Normalization in Preference-based Evolutionary Multi-objective Optimization Using a Reference Point
Ryoji Tanabe
Normalization of objectives plays a crucial role in evolutionary multi-objective optimization (EMO) to handle objective functions with different scales, which can be found in real-…
On the Unbounded External Archive and Population Size in Preference-based Evolutionary Multi-objective Optimization Using a Reference Point
Ryoji Tanabe
Although the population size is an important parameter in evolutionary multi-objective optimization (EMO), little is known about its influence on preference-based EMO (PBEMO). The…
Quality Indicators for Preference-based Evolutionary Multi-objective Optimization Using a Reference Point: A Review and Analysis
Ryoji Tanabe, Ke Li
Some quality indicators have been proposed for benchmarking preference-based evolutionary multi-objective optimization algorithms using a reference point. Although a systematic rev…
Benchmarking the Hooke-Jeeves Method, MTS-LS1, and BSrr on the Large-scale BBOB Function Set
Ryoji Tanabe
This paper investigates the performance of three black-box optimizers exploiting separability on the 24 large-scale BBOB functions, including the Hooke-Jeeves method, MTS-LS1, and…
A Two-phase Framework with a Bézier Simplex-based Interpolation Method for Computationally Expensive Multi-objective Optimization
Ryoji Tanabe, Youhei Akimoto, Ken Kobayashi +3
This paper proposes a two-phase framework with a Bézier simplex-based interpolation method (TPB) for computationally expensive multi-objective optimization. The first phase in TPB…
Benchmarking Feature-based Algorithm Selection Systems for Black-box Numerical Optimization
Ryoji Tanabe
Feature-based algorithm selection aims to automatically find the best one from a portfolio of optimization algorithms on an unseen problem based on its landscape features. Feature-…
Towards Exploratory Landscape Analysis for Large-scale Optimization: A Dimensionality Reduction Framework
Ryoji Tanabe
Although exploratory landscape analysis (ELA) has shown its effectiveness in various applications, most previous studies focused only on low- and moderate-dimensional problems. Thu…
TPAM: A Simulation-Based Model for Quantitatively Analyzing Parameter Adaptation Methods
Ryoji Tanabe, Alex Fukunaga
While a large number of adaptive Differential Evolution (DE) algorithms have been proposed, their Parameter Adaptation Methods (PAMs) are not well understood. We propose a Target f…
Reviewing and Benchmarking Parameter Control Methods in Differential Evolution
Ryoji Tanabe, Alex Fukunaga
Many Differential Evolution (DE) algorithms with various parameter control methods (PCMs) have been proposed. However, previous studies usually considered PCMs to be an integral co…
How Far Are We From an Optimal, Adaptive DE?
Ryoji Tanabe, Alex Fukunaga
We consider how an (almost) optimal parameter adaptation process for an adaptive DE might behave, and compare the behavior and performance of this approximately optimal process to…
An Analysis of Control Parameters of MOEA/D Under Two Different Optimization Scenarios
Ryoji Tanabe, Hisao Ishibuchi
An unbounded external archive (UEA), which stores all nondominated solutions found during the search process, is frequently used to evaluate the performance of multi-objective evol…
Review and Analysis of Three Components of Differential Evolution Mutation Operator in MOEA/D-DE
Ryoji Tanabe, Hisao Ishibuchi
A decomposition-based multi-objective evolutionary algorithm with a differential evolution variation operator (MOEA/D-DE) shows high performance on challenging multi-objective prob…
A Niching Indicator-Based Multi-modal Many-objective Optimizer
Ryoji Tanabe, Hisao Ishibuchi
Multi-modal multi-objective optimization is to locate (almost) equivalent Pareto optimal solutions as many as possible. Some evolutionary algorithms for multi-modal multi-objective…
Non-elitist Evolutionary Multi-objective Optimizers Revisited
Ryoji Tanabe, Hisao Ishibuchi
Since around 2000, it has been considered that elitist evolutionary multi-objective optimization algorithms (EMOAs) always outperform non-elitist EMOAs. This paper revisits the per…
A Framework to Handle Multi-modal Multi-objective Optimization in Decomposition-based Evolutionary Algorithms
Ryoji Tanabe, Hisao Ishibuchi
Multi-modal multi-objective optimization is to locate (almost) equivalent Pareto optimal solutions as many as possible. While decomposition-based evolutionary algorithms have good…
A Review of Evolutionary Multi-modal Multi-objective Optimization
Ryoji Tanabe, Hisao Ishibuchi
Multi-modal multi-objective optimization aims to find all Pareto optimal solutions including overlapping solutions in the objective space. Multi-modal multi-objective optimization…
An Easy-to-use Real-world Multi-objective Optimization Problem Suite
Ryoji Tanabe, Hisao Ishibuchi
Although synthetic test problems are widely used for the performance assessment of evolutionary multi-objective optimization algorithms, they are likely to include unrealistic prop…
An Analysis of Quality Indicators Using Approximated Optimal Distributions in a Three-dimensional Objective Space
Ryoji Tanabe, Hisao Ishibuchi
Although quality indicators play a crucial role in benchmarking evolutionary multi-objective optimization algorithms, their properties are still unclear. One promising approach for…
Analyzing Adaptive Parameter Landscapes in Parameter Adaptation Methods for Differential Evolution
Ryoji Tanabe
Since the scale factor and the crossover rate significantly influence the performance of differential evolution (DE), parameter adaptation methods (PAMs) for the two parameters hav…