What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
Gary G. Yen focuses on Mathematical optimization, Evolutionary algorithm, Evolutionary computation, Multi-objective optimization and Benchmark.
Many of his studies on Mathematical optimization apply to Algorithm as well.
His Evolutionary algorithm research focuses on Performance metric and how it connects with Heuristic, Metric and Tournament selection.
A significant part of his Evolutionary computation research incorporates Artificial intelligence and Machine learning studies.
His study focuses on the intersection of Artificial intelligence and fields such as Pattern recognition with connections in the field of Contextual image classification and Feature.
His Multi-objective optimization study also includes fields such as
- Algorithm design and related Convergence,
- Particle swarm optimization which connect with Swarm behaviour, Transfer of learning and Estimation of distribution algorithm.
His most cited work include:
- Problems with fitting to the power-law distribution (490 citations)
- Wavelet packet feature extraction for vibration monitoring (415 citations)
- A generic framework for constrained optimization using genetic algorithms (269 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Mathematical optimization, Artificial intelligence, Evolutionary algorithm, Artificial neural network and Multi-objective optimization.
As part of the same scientific family, Gary G. Yen usually focuses on Mathematical optimization, concentrating on Benchmark and intersecting with Constrained optimization.
His Artificial intelligence research includes elements of Genetic algorithm, Machine learning, Data mining and Pattern recognition.
His Evolutionary algorithm study deals with Evolutionary computation intersecting with Algorithm design.
His Artificial neural network research integrates issues from Control engineering, Control theory and Control theory.
His Multi-objective optimization study incorporates themes from Performance indicator and Population size.
He most often published in these fields:
- Mathematical optimization (37.30%)
- Artificial intelligence (35.42%)
- Evolutionary algorithm (26.65%)
What were the highlights of his more recent work (between 2017-2021)?
- Mathematical optimization (37.30%)
- Evolutionary algorithm (26.65%)
- Multi-objective optimization (20.38%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Mathematical optimization, Evolutionary algorithm, Multi-objective optimization, Optimization problem and Artificial intelligence.
He specializes in Mathematical optimization, namely Pareto principle.
The various areas that Gary G. Yen examines in his Evolutionary algorithm study include Evolutionary computation, Particle swarm optimization, Algorithm design and Sensitivity.
His Multi-objective optimization study combines topics in areas such as Robust optimization, Metric, Boundary, Benchmark and Evolution strategy.
His Optimization problem research is multidisciplinary, incorporating perspectives in Transfer of learning, Performance indicator, Estimation of distribution algorithm and Approximation algorithm.
His research investigates the link between Artificial intelligence and topics such as Machine learning that cross with problems in Search algorithm and Local search.
Between 2017 and 2021, his most popular works were:
- Evolving Deep Convolutional Neural Networks for Image Classification (111 citations)
- IGD Indicator-Based Evolutionary Algorithm for Many-Objective Optimization Problems (110 citations)
- Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification (99 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
His primary areas of investigation include Artificial intelligence, Mathematical optimization, Evolutionary computation, Evolutionary algorithm and Multi-objective optimization.
Gary G. Yen has researched Artificial intelligence in several fields, including Machine learning and Pattern recognition.
His Mathematical optimization research is multidisciplinary, incorporating elements of Convergence and Robustness.
His studies in Multi-objective optimization integrate themes in fields like Robust optimization, Algorithm design, Cluster analysis and Benchmark.
His Benchmark study combines topics from a wide range of disciplines, such as Genetic algorithm, Mutation operator, Differential evolution and Domain knowledge.
Gary G. Yen has included themes like Estimation of distribution algorithm and Approximation algorithm in his Optimization problem study.
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