What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Mathematical optimization
Multi-objective optimization, Mathematical optimization, Knapsack problem, Pareto principle and Evolutionary algorithm are his primary areas of study.
His Multi-objective optimization research includes elements of Evolutionary computation, Convergence, Approximation algorithm and Space.
His Mathematical optimization research incorporates themes from Function, Algorithm, Algorithm design and Solution set.
In his research on the topic of Knapsack problem, Local optimum is strongly related with Fitness function.
His study on Evolutionary algorithm is covered under Artificial intelligence.
The Artificial intelligence study combines topics in areas such as Parallel algorithm and Machine learning.
His most cited work include:
- Evolutionary many-objective optimization: A short review (598 citations)
- Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning (331 citations)
- Performance of Decomposition-Based Many-Objective Algorithms Strongly Depends on Pareto Front Shapes (253 citations)
What are the main themes of his work throughout his whole career to date?
Yusuke Nojima mostly deals with Artificial intelligence, Mathematical optimization, Multi-objective optimization, Machine learning and Fuzzy logic.
His work in Artificial intelligence addresses subjects such as Data mining, which are connected to disciplines such as Selection.
His research is interdisciplinary, bridging the disciplines of Solution set and Mathematical optimization.
His work in Multi-objective optimization addresses issues such as Maximization, which are connected to fields such as Distribution.
Yusuke Nojima combines subjects such as Classifier, Genetics, Training set and Heuristic with his study of Machine learning.
In his study, Fuzzy number is inextricably linked to Fuzzy classification, which falls within the broad field of Fuzzy rule.
He most often published in these fields:
- Artificial intelligence (48.08%)
- Mathematical optimization (41.11%)
- Multi-objective optimization (34.15%)
What were the highlights of his more recent work (between 2016-2021)?
- Multi-objective optimization (34.15%)
- Mathematical optimization (41.11%)
- Artificial intelligence (48.08%)
In recent papers he was focusing on the following fields of study:
Yusuke Nojima mainly focuses on Multi-objective optimization, Mathematical optimization, Artificial intelligence, Evolutionary algorithm and Pareto principle.
His study in Multi-objective optimization is interdisciplinary in nature, drawing from both Maximization, Distribution, Solution set and Algorithm, Optimization problem.
His study explores the link between Mathematical optimization and topics such as Convergence that cross with problems in Minification.
His Artificial intelligence study combines topics in areas such as Machine learning and Pattern recognition.
His work on Multiobjective optimization problem as part of general Evolutionary algorithm research is frequently linked to Decomposition, Test suite and Human multitasking, bridging the gap between disciplines.
His research in Pareto principle tackles topics such as Boundary which are related to areas like Projection, Ideal and Chebyshev filter.
Between 2016 and 2021, his most popular works were:
- Performance of Decomposition-Based Many-Objective Algorithms Strongly Depends on Pareto Front Shapes (253 citations)
- How to Specify a Reference Point in Hypervolume Calculation for Fair Performance Comparison (59 citations)
- A Framework for Large-Scale Multiobjective Optimization Based on Problem Transformation (48 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
Yusuke Nojima mainly investigates Multi-objective optimization, Mathematical optimization, Evolutionary algorithm, Pareto principle and Solution set.
Yusuke Nojima interconnects Algorithm, Algorithm design, Distribution and Convergence in the investigation of issues within Multi-objective optimization.
His biological study spans a wide range of topics, including Genetic algorithm and Minification.
His Mathematical optimization research integrates issues from Transformation and Grid.
The concepts of his Evolutionary algorithm study are interwoven with issues in Normalization, Computational intelligence, Feasible region, Evolutionary computation and Function.
His biological study deals with issues like Optimization problem, which deal with fields such as Theoretical computer science, Crossover and Space.
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