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.
