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 | 2013 | |
|---|---|---|
| j17 |      | Michela Fazzolari, Rafael Alcalá, Yusuke Nojima, Hisao Ishibuchi, Francisco Herrera: A Review of the Application of Multiobjective Evolutionary Fuzzy Systems: Current Status and Further Directions. IEEE T. Fuzzy Systems 21(1): 45-65 (2013) |
| j16 | | Hisao Ishibuchi, Shingo Mihara, Yusuke Nojima: Parallel Distributed Hybrid Fuzzy GBML Models With Rule Set Migration and Training Data Rotation. IEEE T. Fuzzy Systems 21(2): 355-368 (2013) |
| c72 | | Hisao Ishibuchi, Masakazu Yamane, Yusuke Nojima: Difficulty in Evolutionary Multiobjective Optimization of Discrete Objective Functions with Different Granularities. EMO 2013: 230-245 |
| c71 | | Hisao Ishibuchi, Naoya Akedo, Yusuke Nojima: Relation between Neighborhood Size and MOEA/D Performance on Many-Objective Problems. EMO 2013: 459-474 |
| 2012 | ||
| c70 | | Hisao Ishibuchi, Koichiro Hoshino, Yusuke Nojima: Strategy evolution in a spatial IPD game where each agent is not allowed to play against itself. IEEE Congress on Evolutionary Computation 2012: 1-8 |
| c69 | | Hisao Ishibuchi, Koichiro Hoshino, Yusuke Nojima: Evolution of strategies in a spatial IPD game with a number of different representation schemes. IEEE Congress on Evolutionary Computation 2012: 1-8 |
| c68 | | Yusuke Nojima, Shingo Mihara, Hisao Ishibuchi: Application of parallel distributed genetics-based machine learning to imbalanced data sets. FUZZ-IEEE 2012: 1-6 |
| c67 | | Hisao Ishibuchi, Masakazu Yamane, Yusuke Nojima: Effects of discrete objective functions with different granularities on the search behavior of EMO algorithms. GECCO 2012: 481-488 |
| c66 | | Hisao Ishibuchi, Naoya Akedo, Yusuke Nojima: Recombination of Similar Parents in SMS-EMOA on Many-Objective 0/1 Knapsack Problems. PPSN (2) 2012: 132-142 |
| c65 | | Hisao Ishibuchi, Masakazu Yamane, Yusuke Nojima: Ensemble Fuzzy Rule-Based Classifier Design by Parallel Distributed Fuzzy GBML Algorithms. SEAL 2012: 93-103 |
| 2011 | ||
| j15 | | Hisao Ishibuchi, Yutaka Kaisho, Yusuke Nojima: Design of Linguistically Interpretable Fuzzy Rule-Based Classifiers: A Short Review and Open Questions. Multiple-Valued Logic and Soft Computing 17(2-3): 101-134 (2011) |
| j14 | | Hisao Ishibuchi, Yuji Sakane, Noritaka Tsukamoto, Yusuke Nojima: Implementation of cellular genetic algorithms with two neighborhood structures for single-objective and multi-objective optimization. Soft Comput. 15(9): 1749-1767 (2011) |
| j13 | | Yusuke Nojima, Rafael Alcalá, Hisao Ishibuchi, Francisco Herrera: Special issue on evolutionary fuzzy systems. Soft Comput. 15(12): 2299-2301 (2011) |
| j12 | | Rafael Alcalá, Yusuke Nojima, Francisco Herrera, Hisao Ishibuchi: Multiobjective genetic fuzzy rule selection of single granularity-based fuzzy classification rules and its interaction with the lateral tuning of membership functions. Soft Comput. 15(12): 2303-2318 (2011) |
| j11 | | Hisao Ishibuchi, Yusuke Nakashima, Yusuke Nojima: Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning. Soft Comput. 15(12): 2415-2434 (2011) |
| j10 | | Hisao Ishibuchi, Hajime Ohyanagi, Yusuke Nojima: Evolution of Strategies With Different Representation Schemes in a Spatial Iterated Prisoner's Dilemma Game. IEEE Trans. Comput. Intellig. and AI in Games 3(1): 67-82 (2011) |
| c64 | | Hisao Ishibuchi, Naoya Akedo, Hiroyuki Ohyanagi, Yusuke Nojima: Behavior of EMO algorithms on many-objective optimization problems with correlated objectives. IEEE Congress on Evolutionary Computation 2011: 1465-1472 |
| c63 | | Hisao Ishibuchi, Keisuke Takahashi, Kouichirou Hoshino, Junpei Maeda, Yusuke Nojima: Effects of configuration of agents with different strategy representations on the evolution of cooperative behavior in a spatial IPD game. CIG 2011: 313-320 |
| c62 | | Hisao Ishibuchi, Yasuhiro Hitotsuyanagi, Hiroyuki Ohyanagi, Yusuke Nojima: Effects of the Existence of Highly Correlated Objectives on the Behavior of MOEA/D. EMO 2011: 166-181 |
| c61 | | Yusuke Nojima, Shinya Nishikawa, Hisao Ishibuchi: A meta-fuzzy classifier for specifying appropriate fuzzy partitions by genetic fuzzy rule selection with data complexity measures. FUZZ-IEEE 2011: 264-271 |
| c60 | | Hisao Ishibuchi, Yusuke Nojima: Toward quantitative definition of explanation ability of fuzzy rule-based classifiers. FUZZ-IEEE 2011: 549-556 |
| c59 | | Hisao Ishibuchi, Naoya Akedo, Yusuke Nojima: A many-objective test problem for visually examining diversity maintenance behavior in a decision space. GECCO 2011: 649-656 |
| c58 | | Yusuke Nojima, Hisao Ishibuchi: Mobile Robot Controller Design by Evolutionary Multiobjective Optimization in Multiagent Environments. ICIRA (2) 2011: 515-524 |
| c57 | | Hisao Ishibuchi, Shingo Mihara, Yusuke Nojima: Training Data Subdivision and Periodical Rotation in Hybrid Fuzzy Genetics-Based Machine Learning. ICMLA (1) 2011: 229-234 |
| 2010 | ||
| j9 | | Hisao Ishibuchi, Noritaka Tsukamoto, Yusuke Nojima: Diversity Improvement by Non-Geometric Binary Crossover in Evolutionary Multiobjective Optimization. IEEE Trans. Evolutionary Computation 14(6): 985-998 (2010) |
| c56 | | Yusuke Nojima, Shingo Mihara, Hisao Ishibuchi: Ensemble classifier design by parallel distributed implementation of genetic fuzzy rule selection for large data sets. IEEE Congress on Evolutionary Computation 2010: 1-8 |
| c55 | | Hisao Ishibuchi, Yusuke Nakashima, Yusuke Nojima: Effects of fine fuzzy partitions on the generalization ability of evolutionary multi-objective fuzzy rule-based classifiers. FUZZ-IEEE 2010: 1-8 |
| c54 | | Yusuke Nojima, Yutaka Kaisho, Hisao Ishibuchi: Accuracy improvement of genetic fuzzy rule selection with candidate rule addition and membership tuning. FUZZ-IEEE 2010: 1-8 |
| c53 | | Hisao Ishibuchi, Yuji Sakane, Noritaka Tsukamoto, Yusuke Nojima: Simultaneous use of different scalarizing functions in MOEA/D. GECCO 2010: 519-526 |
| c52 | | Hisao Ishibuchi, Noritaka Tsukamoto, Yuji Sakane, Yusuke Nojima: Indicator-based evolutionary algorithm with hypervolume approximation by achievement scalarizing functions. GECCO 2010: 527-534 |
| c51 | | Hisao Ishibuchi, Yasuhiro Hitotsuyanagi, Yusuke Nakashima, Yusuke Nojima: Multiobjectivization from two objectives to four objectives in evolutionary multi-objective optimization algorithms. NaBIC 2010: 502-507 |
| c50 | | Shinya Nishikawa, Yusuke Nojima, Hisao Ishibuchi: Appropriate granularity specification for fuzzy classifier design by data complexity measures. NaBIC 2010: 691-696 |
| c49 | | Hisao Ishibuchi, Yasuhiro Hitotsuyanagi, Noritaka Tsukamoto, Yusuke Nojima: Many-Objective Test Problems to Visually Examine the Behavior of Multiobjective Evolution in a Decision Space. PPSN (2) 2010: 91-100 |
| c48 | | Hisao Ishibuchi, Yasuhiro Hitotsuyanagi, Yoshihiko Wakamatsu, Yusuke Nojima: How to Choose Solutions for Local Search in Multiobjective Combinatorial Memetic Algorithms. PPSN (1) 2010: 516-525 |
| c47 | | Yusuke Nojima, Shingo Mihara, Hisao Ishibuchi: Parallel Distributed Implementation of Genetics-Based Machine Learning for Fuzzy Classifier Design. SEAL 2010: 309-318 |
| 2009 | ||
| j8 | | Rafael Alcalá, Yusuke Nojima: Special issue on genetic fuzzy systems: new advances. Evolutionary Intelligence 2(1-2): 1-3 (2009) |
| j7 | | Yusuke Nojima, Hisao Ishibuchi, Isao Kuwajima: Parallel distributed genetic fuzzy rule selection. Soft Comput. 13(5): 511-519 (2009) |
| j6 | | Hisao Ishibuchi, Yasuhiro Hitotsuyanagi, Noritaka Tsukamoto, Yusuke Nojima: Use of biased neighborhood structures in multiobjective memetic algorithms. Soft Comput. 13(8-9): 795-810 (2009) |
| c46 | | Hisao Ishibuchi, Noritaka Tsukamoto, Yuji Sakane, Yusuke Nojima: Hypervolume approximation using achievement scalarizing functions for evolutionary many-objective optimization. IEEE Congress on Evolutionary Computation 2009: 530-537 |
| c45 | | Hisao Ishibuchi, Yuji Sakane, Noritaka Tsukamoto, Yusuke Nojima: Effects of using two neighborhood structures on the performance of cellular evolutionary algorithms for many-objective optimization. IEEE Congress on Evolutionary Computation 2009: 2508-2515 |
| c44 | | Hisao Ishibuchi, Yuji Sakane, Noritaka Tsukamoto, Yusuke Nojima: Adaptation of Scalarizing Functions in MOEA/D: An Adaptive Scalarizing Function-Based Multiobjective Evolutionary Algorithm. EMO 2009: 438-452 |
| c43 | | Hisao Ishibuchi, Yusuke Nojima: Discussions on Interpretability of Fuzzy Systems using Simple Examples. IFSA/EUSFLAT Conf. 2009: 1649-1654 |
| c42 | | Yusuke Nojima, Hisao Ishibuchi: Interactive Fuzzy Modeling by Evolutionary Multiobjective Optimization with User Preference. IFSA/EUSFLAT Conf. 2009: 1839-1844 |
| c41 | | Hisao Ishibuchi, Hiroyuki Ohyanagi, Yusuke Nojima: Evolution of cooperative behavior in a spatial iterated prisoner's dilemma game with different representation schemes of game strategies. FUZZ-IEEE 2009: 1568-1573 |
| c40 | | Hisao Ishibuchi, Yuji Sakane, Noritaka Tsukamoto, Yusuke Nojima: Selecting a small number of representative non-dominated solutions by a hypervolume-based solution selection approach. FUZZ-IEEE 2009: 1609-1614 |
| c39 | | Rafael Alcalá, Yusuke Nojima, Francisco Herrera, Hisao Ishibuchi: Generating single granularity-based fuzzy classification rules for multiobjective genetic fuzzy rule selection. FUZZ-IEEE 2009: 1718-1723 |
| c38 | | Hisao Ishibuchi, Yusuke Nakashima, Yusuke Nojima: Search ability of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning. FUZZ-IEEE 2009: 1724-1729 |
| c37 | | Hisao Ishibuchi, Yutaka Kaisho, Yusuke Nojima: Complexity, interpretability and explanation capability of fuzzy rule-based classifiers. FUZZ-IEEE 2009: 1730-1735 |
| c36 | | Hisao Ishibuchi, Yuji Sakane, Noritaka Tsukamoto, Yusuke Nojima: Single-objective and multi-objective formulations of solution selection for hypervolume maximization. GECCO 2009: 1831-1832 |
| c35 | | Yusuke Nojima, Hisao Ishibuchi: Effects of Data Reduction on the Generalization Ability of Parallel Distributed Genetic Fuzzy Rule Selection. ISDA 2009: 96-101 |
| c34 | | Hisao Ishibuchi, Yuji Sakane, Noritaka Tsukamoto, Yusuke Nojima: Evolutionary Many-Objective Optimization by NSGA-II and MOEA/D with Large Populations. SMC 2009: 1758-1763 |
| c33 | | Yusuke Nojima, Yusuke Nakashima, Hisao Ishibuchi: Effects of the Use of Multiple Fuzzy Partitions on the Search Ability of Multiobjective Fuzzy Genetics-Based Machine Learning. SoCPaR 2009: 341-346 |
| c32 | | Yuki Tsujimoto, Yasuhiro Hitotsuyanagi, Yusuke Nojima, Hisao Ishibuchi: Effects of Including Single-Objective Optimal Solutions in an Initial Population on Evolutionary Multiobjective Optimization. SoCPaR 2009: 352-357 |
| c31 | | Hisao Ishibuchi, Noritaka Tsukamoto, Yusuke Nojima: Empirical Analysis of Using Weighted Sum Fitness Functions in NSGA-II for Many-Objective 0/1 Knapsack Problems. UKSim 2009: 71-76 |
| 2008 | ||
| j5 | | Hisao Ishibuchi, Kaname Narukawa, Noritaka Tsukamoto, Yusuke Nojima: An empirical study on similarity-based mating for evolutionary multiobjective combinatorial optimization. European Journal of Operational Research 188(1): 57-75 (2008) |
| c30 | | Hisao Ishibuchi, Noritaka Tsukamoto, Yusuke Nojima: Evolutionary many-objective optimization: A short review. IEEE Congress on Evolutionary Computation 2008: 2419-2426 |
| c29 | | Hisao Ishibuchi, Yasuhiro Hitotsuyanagi, Yusuke Nojima: Scalability of multiobjective genetic local search to many-objective problems: Knapsack problem case studies. IEEE Congress on Evolutionary Computation 2008: 3586-3593 |
| c28 | | Isao Kuwajima, Hisao Ishibuchi, Yusuke Nojima: Effectiveness of designing fuzzy rule-based classifiers from Pareto-optimal rules. FUZZ-IEEE 2008: 1185-1192 |
| c27 | | Hisao Ishibuchi, Noritaka Tsukamoto, Yasuhiro Hitotsuyanagi, Yusuke Nojima: Effectiveness of scalability improvement attempts on the performance of NSGA-II for many-objective problems. GECCO 2008: 649-656 |
| c26 | | Hisao Ishibuchi, Noritaka Tsukamoto, Yusuke Nojima: Maintaining the diversity of solutions by non-geometric binary crossover: a worst one-max solver competition case study. GECCO 2008: 1111-1112 |
| c25 | | Yusuke Nojima, Hisao Ishibuchi: Effects of Diversity Measures on the Design of Ensemble Classifiers by Multiobjective Genetic Fuzzy Rule Selection with a Multi-classifier Coding Scheme. HAIS 2008: 755-763 |
| c24 | | Hisao Ishibuchi, Noritaka Tsukamoto, Yusuke Nojima: Examining the Effect of Elitism in Cellular Genetic Algorithms Using Two Neighborhood Structures. PPSN 2008: 458-467 |
| c23 | | Hisao Ishibuchi, Yasuhiro Hitotsuyanagi, Noritaka Tsukamoto, Yusuke Nojima: Use of Heuristic Local Search for Single-Objective Optimization in Multiobjective Memetic Algorithms. PPSN 2008: 743-752 |
| c22 | | Hisao Ishibuchi, Noritaka Tsukamoto, Yusuke Nojima: Use of Local Ranking in Cellular Genetic Algorithms with Two Neighborhood Structures. SEAL 2008: 309-318 |
| c21 | | Hisao Ishibuchi, Noritaka Tsukamoto, Yusuke Nojima: Behavior of Evolutionary Many-Objective Optimization. UKSim 2008: 266-271 |
| p5 | | Hisao Ishibuchi, Isao Kuwajima, Yusuke Nojima: Evolutionary Multi-objective Rule Selection for Classification Rule Mining. Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases 2008: 47-70 |
| p4 | | Hisao Ishibuchi, Yusuke Nojima, Isao Kuwajima: Evolutionary Multiobjective Design of Fuzzy Rule-Based Classifiers. Computational Intelligence: A Compendium 2008: 641-685 |
| p3 | | Hisao Ishibuchi, Yusuke Nojima: Pattern Classification with Linguistic Rules. Fuzzy Sets and Their Extensions: Representation, Aggregation and Models 2008: 377-395 |
| 2007 | ||
| j4 | | Hisao Ishibuchi, Yusuke Nojima: Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning. Int. J. Approx. Reasoning 44(1): 4-31 (2007) |
| j3 | | Yusuke Nojima, Hisao Ishibuchi: Genetic rule selection with a multi-classifier coding scheme for ensemble classifier design. Int. J. Hybrid Intell. Syst. 4(3): 157-169 (2007) |
| c20 | | Hisao Ishibuchi, Yasuhiro Hitotsuyanagi, Yusuke Nojima: An empirical study on the specification of the local search application probability in multiobjective memetic algorithms. IEEE Congress on Evolutionary Computation 2007: 2788-2795 |
| c19 | | Hisao Ishibuchi, Noritaka Tsukamoto, Yusuke Nojima: Iterative approach to indicator-based multiobjective optimization. IEEE Congress on Evolutionary Computation 2007: 3967-3974 |
| c18 | | Ken Ohara, Yusuke Nojima, Yumeka Kitano, Hisao Ishibuchi: Effects of spatial structures on evolution of iterated prisoner's dilemma game strategies with probabilistic decision making. IEEE Congress on Evolutionary Computation 2007: 4051-4058 |
| c17 | | Yusuke Nojima, Isao Kuwajima, Hisao Ishibuchi: Data Set Subdivision for Parallel Distributed Implementation of Genetic Fuzzy Rule Selection. FUZZ-IEEE 2007: 1-6 |
| c16 | | Hisao Ishibuchi, Yusuke Nojima, Noritaka Tsukamoto, Ken Ohara: Effects of the use of non-geometric binary crossover on evolutionary multiobjective optimization. GECCO 2007: 829-836 |
| c15 | | Hisao Ishibuchi, Isao Kuwajima, Yusuke Nojima: Prescreening of Candidate Rules Using Association Rule Mining and Pareto-optimality in Genetic Rule Selection. KES (2) 2007: 509-516 |
| c14 | | Hisao Ishibuchi, Noritaka Tsukamoto, Yusuke Nojima: Choosing extreme parents for diversity improvement in evolutionary multiobjective optimization algorithms. SMC 2007: 1946-1951 |
| p2 | | Hisao Ishibuchi, Isao Kuwajima, Yusuke Nojima: Use of Pareto-Optimal and Near Pareto-Optimal Candidate Rules in Genetic Fuzzy Rule Selection. Analysis and Design of Intelligent Systems using Soft Computing Techniques 2007: 387-396 |
| 2006 | ||
| j2 | | Hisao Ishibuchi, Yusuke Nojima: Evolutionary multiobjective optimization for the design of fuzzy rule-based ensemble classifiers. Int. J. Hybrid Intell. Syst. 3(3): 129-145 (2006) |
| j1 | | Naoyuki Kubota, Yusuke Nojima, Fumio Kojima, Toshio Fukuda: Multiple fuzzy state-value functions for human evaluation through interactive trajectory planning of a partner robot. Soft Comput. 10(10): 891-901 (2006) |
| c13 | | Hisao Ishibuchi, Yusuke Nojima: Optimization of Scalarizing Functions Through Evolutionary Multiobjective Optimization. EMO 2006: 51-65 |
| c12 | | Hisao Ishibuchi, Yusuke Nojima, Kaname Narukawa, Tsutomu Doi: Incorporation of decision maker's preference into evolutionary multiobjective optimization algorithms. GECCO 2006: 741-742 |
| c11 | | Hisao Ishibuchi, Yusuke Nojima, Isao Kuwajima: Multiobjective genetic rule selection as a data mining postprocessing procedure. GECCO 2006: 1591-1592 |
| c10 | | Yusuke Nojima, Hisao Ishibuchi: Designing Fuzzy Ensemble Classifiers by Evolutionary Multiobjective Optimization with an Entropy-Based Diversity Criterion. HIS 2006: 59 |
| c9 | | Hisao Ishibuchi, Yusuke Nojima, Isao Kuwajima: Finding Simple Fuzzy Classification Systems with High Interpretability Through Multiobjective Rule Selection. KES (2) 2006: 86-93 |
| c8 | | Hisao Ishibuchi, Tsutomu Doi, Yusuke Nojima: Incorporation of Scalarizing Fitness Functions into Evolutionary Multiobjective Optimization Algorithms. PPSN 2006: 493-502 |
| c7 | | Hisao Ishibuchi, Tsutomu Doi, Yusuke Nojima: Effects of Using Two Neighborhood Structures in Cellular Genetic Algorithms for Function Optimization. PPSN 2006: 949-958 |
| p1 | | Hisao Ishibuchi, Yusuke Nojima: Fuzzy Ensemble Design through Multi-Objective Fuzzy Rule Selection. Multi-Objective Machine Learning 2006: 507-530 |
| 2005 | ||
| c6 | | Yusuke Nojima, Kaname Narukawa, Shiori Kaige, Hisao Ishibuchi: Effects of Removing Overlapping Solutions on the Performance of the NSGA-II Algorithm. EMO 2005: 341-354 |
| c5 | | Hisao Ishibuchi, Yusuke Nojima: Multiobjective Formulations of Fuzzy Rule-Based Classification System Design. EUSFLAT Conf. 2005: 285-290 |
| c4 | | Hisao Ishibuchi, Kaname Narukawa, Yusuke Nojima: An empirical study on the handling of overlapping solutions in evolutionary multiobjective optimization. GECCO 2005: 817-824 |
| c3 | | Hisao Ishibuchi, Yusuke Nojima: Performance Evaluation of Evolutionary Multiobjective Approaches to the Design of Fuzzy Rule-Based Ensemble Classifiers. HIS 2005: 271-276 |
| 2004 | ||
| c2 | | Naoyuki Kubota, Yusuke Nojima, Fumio Kojima: Imitative behavior generation for a vision-based partner robot. IROS 2004: 3080-3085 |
| 2000 | ||
| c1 | | Naoyuki Kubota, Yusuke Nojima, Fumio Kojima, Toshio Fukuda: Multi-Objective Behavior Coordinate for a Mobile Robot with Fuzzy Neural Networks. IJCNN (6) 2000: 311-316 |
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