What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Kenneth de Jong spends much of his time researching Artificial intelligence, Machine learning, Evolutionary algorithm, Crossover and Mathematical optimization.
His Artificial intelligence research incorporates elements of Genetic algorithm, Function optimization, Field and Coevolution.
His biological study spans a wide range of topics, including Variety and Perspective.
The Evolutionary algorithm study combines topics in areas such as Evolutionary computation and Econometrics.
His work deals with themes such as Complex system, Theoretical computer science, Order and Computation, which intersect with Crossover.
His Mathematical optimization study integrates concerns from other disciplines, such as Context, Maximum satisfiability problem and Problem domain.
His most cited work include:
- A Cooperative Coevolutionary Approach to Function Optimization (1158 citations)
- Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents (986 citations)
- Evolutionary computation: a unified approach (751 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Evolutionary algorithm, Speech recognition, Machine learning and Vowel.
His research is interdisciplinary, bridging the disciplines of Pattern recognition and Artificial intelligence.
His study in Evolutionary algorithm is interdisciplinary in nature, drawing from both Evolutionary computation, Genetic algorithm and Fitness landscape.
Kenneth de Jong works mostly in the field of Speech recognition, limiting it down to topics relating to Variation and, in certain cases, Context.
His work on Learning classifier system and Computational learning theory as part of general Machine learning study is frequently connected to Set, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
The various areas that Kenneth de Jong examines in his Vowel study include American English, Categorization and Vocal tract.
He most often published in these fields:
- Artificial intelligence (32.14%)
- Evolutionary algorithm (26.19%)
- Speech recognition (17.06%)
What were the highlights of his more recent work (between 2013-2021)?
- Evolutionary algorithm (26.19%)
- Artificial intelligence (32.14%)
- Speech recognition (17.06%)
In recent papers he was focusing on the following fields of study:
Evolutionary algorithm, Artificial intelligence, Speech recognition, Theoretical computer science and Machine learning are his primary areas of study.
His Evolutionary algorithm research is multidisciplinary, incorporating elements of Evolutionary computation, Path and Fitness landscape.
His studies deal with areas such as Biomolecule, Data mining and Natural language processing as well as Artificial intelligence.
Kenneth de Jong has researched Speech recognition in several fields, including Affect, Mandarin Chinese and Identification.
The concepts of his Theoretical computer science study are interwoven with issues in Boolean circuit, Key, Space and Cartesian genetic programming.
Kenneth de Jong interconnects Scalability, Meta learning, Function and Modular design in the investigation of issues within Machine learning.
Between 2013 and 2021, his most popular works were:
- Effective Automated Feature Construction and Selection for Classification of Biological Sequences (38 citations)
- Understanding Simple Asynchronous Evolutionary Algorithms (19 citations)
- Computing energy landscape maps and structural excursions of proteins (17 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Kenneth de Jong mostly deals with Evolutionary algorithm, Artificial intelligence, Protein structure prediction, Theoretical computer science and Energy landscape.
His Evolutionary algorithm study is related to the wider topic of Mathematical optimization.
The study incorporates disciplines such as Machine learning and Continuous optimization in addition to Artificial intelligence.
The various areas that Kenneth de Jong examines in his Continuous optimization study include Artificial neural network, Feature learning and Control.
His Theoretical computer science study incorporates themes from Parameter space, Space and Encoding.
His study in Feature is interdisciplinary in nature, drawing from both Discretization, Sequence, Genetic programming, Series and Pattern recognition.
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