What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
His scientific interests lie mostly in Artificial intelligence, Genetic programming, Machine learning, Mathematical optimization and Algorithm.
Hitoshi Iba combines subjects such as Genetic learning and Embedded system with his study of Artificial intelligence.
His biological study spans a wide range of topics, including Programming language, Training set, Genetic algorithm and Differential equation.
His Machine learning research incorporates themes from Resampling and Gene regulatory network.
Hitoshi Iba mostly deals with Evolutionary algorithm in his studies of Mathematical optimization.
His Evolutionary algorithm study combines topics in areas such as Particle swarm optimization, Differential evolution and Metaheuristic.
His most cited work include:
- Accelerating Differential Evolution Using an Adaptive Local Search (519 citations)
- Particle swarm optimization with Gaussian mutation (476 citations)
- Differential evolution for economic load dispatch problems (359 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Genetic programming, Machine learning, Evolutionary computation and Mathematical optimization.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Genetic algorithm and Gene regulatory network.
Hitoshi Iba studied Gene regulatory network and Data mining that intersect with Gene expression.
The study incorporates disciplines such as Algorithm, Theoretical computer science and Evolutionary programming in addition to Genetic programming.
His Machine learning study combines topics from a wide range of disciplines, such as Classifier, Probabilistic logic, Estimation of distribution algorithm and Gene.
Mathematical optimization is closely attributed to Selection in his work.
He most often published in these fields:
- Artificial intelligence (54.17%)
- Genetic programming (35.58%)
- Machine learning (29.17%)
What were the highlights of his more recent work (between 2012-2020)?
- Artificial intelligence (54.17%)
- Evolutionary computation (16.99%)
- Machine learning (29.17%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Evolutionary computation, Machine learning, Gene regulatory network and Genetic programming.
His Artificial intelligence research incorporates elements of Field, Genetic algorithm, Synthetic biology and Computer vision.
His Evolutionary computation research includes themes of Evolutionary biology, Boosting, Particle swarm optimization and Library science.
He interconnects Classifier and Estimation of distribution algorithm in the investigation of issues within Machine learning.
His Gene regulatory network research includes elements of Bioinformatics, Inference, Evolutionary algorithm, Computational biology and Complex network.
His research integrates issues of Algorithm, Probabilistic logic, Set and Relevance vector machine in his study of Genetic programming.
Between 2012 and 2020, his most popular works were:
- Reverse Engineering of Gene Regulatory Networks Using Dissipative Particle Swarm Optimization (41 citations)
- Reconstruction of Gene Regulatory Networks from Gene Expression Data Using Decoupled Recurrent Neural Network Model (37 citations)
- An Effective Method for Evolving Reaction Networks in Synthetic Biochemical Systems (25 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
The scientist’s investigation covers issues in Artificial intelligence, Gene regulatory network, Genetic programming, Machine learning and Evolutionary algorithm.
His work carried out in the field of Artificial intelligence brings together such families of science as Field, Web search engine and Modeling and simulation.
His work deals with themes such as Set, Relevance, Search engine and Ranking SVM, which intersect with Genetic programming.
His work in Set addresses subjects such as Selection, which are connected to disciplines such as Mathematical optimization.
His studies in Machine learning integrate themes in fields like Probabilistic logic and Data mining.
In Evolutionary algorithm, he works on issues like Algorithm, which are connected to Nonlinear system and Relevance vector machine.
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