H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 73 Citations 26,330 536 World Ranking 671 National Ranking 54

Research.com Recognitions

Awards & Achievements

2019 - Fuzzy Systems Pioneer Award, IEEE Computational Intelligence Society

2014 - IEEE Fellow For contributions to evolutionary multiobjective optimization and fuzzy rule-based classifier design

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

His primary areas of study are Artificial intelligence, Mathematical optimization, Fuzzy classification, Fuzzy set operations and Neuro-fuzzy. His research in Artificial intelligence intersects with topics in Machine learning and Data mining. His research integrates issues of Algorithm and Job shop scheduling in his study of Mathematical optimization.

His Fuzzy classification research incorporates themes from Fuzzy number and Defuzzification. He has researched Defuzzification in several fields, including Fuzzy mathematics, Fuzzy associative matrix and Type-2 fuzzy sets and systems. His Fuzzy set operations study is associated with Fuzzy set.

His most cited work include:

  • A multi-objective genetic local search algorithm and its application to flowshop scheduling (844 citations)
  • Selecting fuzzy if-then rules for classification problems using genetic algorithms (681 citations)
  • Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling (667 citations)

What are the main themes of his work throughout his whole career to date?

The scientist’s investigation covers issues in Artificial intelligence, Mathematical optimization, Fuzzy logic, Multi-objective optimization and Fuzzy classification. His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning, Data mining and Pattern recognition. His research on Mathematical optimization frequently connects to adjacent areas such as Algorithm.

The study incorporates disciplines such as Artificial neural network and Knowledge-based systems in addition to Fuzzy logic. The various areas that he examines in his Fuzzy classification study include Fuzzy number, Defuzzification and Fuzzy set operations. His Fuzzy set operations research focuses on Neuro-fuzzy and how it relates to Adaptive neuro fuzzy inference system.

He most often published in these fields:

  • Artificial intelligence (48.90%)
  • Mathematical optimization (35.95%)
  • Fuzzy logic (26.45%)

What were the highlights of his more recent work (between 2015-2021)?

  • Mathematical optimization (35.95%)
  • Multi-objective optimization (26.45%)
  • Evolutionary algorithm (14.60%)

In recent papers he was focusing on the following fields of study:

His main research concerns Mathematical optimization, Multi-objective optimization, Evolutionary algorithm, Algorithm and Pareto principle. His research in the fields of Evolutionary computation and Optimization problem overlaps with other disciplines such as Modal. The Multi-objective optimization study combines topics in areas such as Maximization, Space, Distribution, Selection and Solution set.

His work deals with themes such as Function, Grid, Point and Normalization, which intersect with Evolutionary algorithm. When carried out as part of a general Algorithm research project, his work on Algorithm design is frequently linked to work in Weight, therefore connecting diverse disciplines of study. His Pareto principle study combines topics in areas such as Linear programming, Boundary and Simplex.

Between 2015 and 2021, his most popular works were:

  • Performance of Decomposition-Based Many-Objective Algorithms Strongly Depends on Pareto Front Shapes (253 citations)
  • Localized Weighted Sum Method for Many-Objective Optimization (146 citations)
  • Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results (63 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Mathematical optimization

Hisao Ishibuchi spends much of his time researching Mathematical optimization, Multi-objective optimization, Evolutionary algorithm, Evolutionary computation and Algorithm. His Mathematical optimization research includes themes of Function, Convergence and Point. His Multi-objective optimization research is multidisciplinary, relying on both Minification, Pareto principle, Selection, Solution set and Benchmark.

His research on Evolutionary computation also deals with topics like

  • Space, which have a strong connection to Pareto optimal,
  • Linear programming which is related to area like Machine learning, Artificial intelligence, Benchmarking, Dimension and Group. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Microeconomics and Dictator game. His Fuzzy classification research is multidisciplinary, incorporating perspectives in Fuzzy number, Interpretability and Adaptive neuro fuzzy inference system.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

A multi-objective genetic local search algorithm and its application to flowshop scheduling

H. Ishibuchi;T. Murata.
systems man and cybernetics (1998)

1249 Citations

Evolutionary many-objective optimization: A short review

H. Ishibuchi;N. Tsukamoto;Y. Nojima.
world congress on computational intelligence (2008)

999 Citations

Selecting fuzzy if-then rules for classification problems using genetic algorithms

H. Ishibuchi;K. Nozaki;N. Yamamoto;H. Tanaka.
IEEE Transactions on Fuzzy Systems (1995)

947 Citations

Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling

H. Ishibuchi;T. Yoshida;T. Murata.
IEEE Transactions on Evolutionary Computation (2003)

881 Citations

Multiobjective programming in optimization of the interval objective function

Hisao Ishibuchi;Hideo Tanaka.
European Journal of Operational Research (1990)

836 Citations

Evolutionary many-objective optimization

H. Ishibuchi;N. Tsukamoto;Y. Nojima.
2008 3rd International Workshop on Genetic and Evolving Systems (2008)

751 Citations

Multi-objective genetic algorithm and its applications to flowshop scheduling

Tadahiko Murata;Hisao Ishibuchi;Hideo Tanaka.
Computers & Industrial Engineering (1996)

681 Citations

Distributed representation of fuzzy rules and its application to pattern classification

Hisao Ishibuchi;Ken Nozaki;Hideo Tanaka.
Fuzzy Sets and Systems (1992)

671 Citations

Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems

H. Ishibuchi;T. Nakashima;T. Murata.
systems man and cybernetics (1999)

652 Citations

Classification and Modeling with Linguistic Information Granules: Advanced Approaches to Linguistic Data Mining

Hisao Ishibuchi.
(2004)

552 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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