2023 - Research.com Computer Science in China Leader Award
2019 - Fuzzy Systems Pioneer Award, IEEE Computational Intelligence Society
2014 - IEEE Fellow For contributions to evolutionary multiobjective optimization and fuzzy rule-based classifier design
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.
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.
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.
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
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.
A multi-objective genetic local search algorithm and its application to flowshop scheduling
H. Ishibuchi;T. Murata.
systems man and cybernetics (1998)
Evolutionary many-objective optimization: A short review
H. Ishibuchi;N. Tsukamoto;Y. Nojima.
world congress on computational intelligence (2008)
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)
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)
Multiobjective programming in optimization of the interval objective function
Hisao Ishibuchi;Hideo Tanaka.
European Journal of Operational Research (1990)
Evolutionary many-objective optimization
H. Ishibuchi;N. Tsukamoto;Y. Nojima.
2008 3rd International Workshop on Genetic and Evolving Systems (2008)
Multi-objective genetic algorithm and its applications to flowshop scheduling
Tadahiko Murata;Hisao Ishibuchi;Hideo Tanaka.
Computers & Industrial Engineering (1996)
Distributed representation of fuzzy rules and its application to pattern classification
Hisao Ishibuchi;Ken Nozaki;Hideo Tanaka.
Fuzzy Sets and Systems (1992)
Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems
H. Ishibuchi;T. Nakashima;T. Murata.
systems man and cybernetics (1999)
Rule weight specification in fuzzy rule-based classification systems
H. Ishibuchi;T. Yamamoto.
IEEE Transactions on Fuzzy Systems (2005)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: