What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Genetic algorithm
His primary areas of investigation include Artificial intelligence, Machine learning, Genetic algorithm, Task and World Wide Web.
His study in Index extends to Artificial intelligence with its themes.
His Machine learning research is multidisciplinary, relying on both Coevolution and Identification.
His Premature convergence study, which is part of a larger body of work in Genetic algorithm, is frequently linked to Binary number and Genetic hitchhiking, bridging the gap between disciplines.
Along with Task, other disciplines of study including Process, Cognition, Perspective, Search engine technology and Meaning are integrated into his research.
His work carried out in the field of World Wide Web brings together such families of science as Artificial neural network and Adaptive algorithm.
His most cited work include:
- AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility (9267 citations)
- Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function (7789 citations)
- New methods for competitive coevolution (500 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Machine learning, Genetic algorithm, Information retrieval and Artificial neural network.
His Artificial intelligence research includes elements of Pattern recognition and Natural language processing.
His Machine learning research is multidisciplinary, incorporating perspectives in Set and Robustness.
His research in Genetic algorithm focuses on subjects like Algorithm, which are connected to Mathematical optimization.
Within one scientific family, Richard K. Belew focuses on topics pertaining to Connectionism under Information retrieval, and may sometimes address concerns connected to Legal information retrieval.
His Artificial neural network study combines topics in areas such as Robot and Reinforcement learning.
He most often published in these fields:
- Artificial intelligence (39.76%)
- Machine learning (22.89%)
- Genetic algorithm (16.87%)
What were the highlights of his more recent work (between 2002-2018)?
- Information retrieval (14.46%)
- AutoDock (7.23%)
- Context (7.23%)
In recent papers he was focusing on the following fields of study:
Richard K. Belew mainly investigates Information retrieval, AutoDock, Context, Ligand and Virtual screening.
His research integrates issues of Categorization and Feature in his study of Information retrieval.
His AutoDock research overlaps with other disciplines such as Searching the conformational space for docking, Combinatorial chemistry and Cluster analysis.
His research in Searching the conformational space for docking intersects with topics in Graphical user interface and Lead Finder, Protein–ligand docking.
In his research, Set and Machine learning is intimately related to Molecular Docking Simulation, which falls under the overarching field of Combinatorial chemistry.
His Virtual screening research focuses on Fragment and how it connects with Artificial intelligence.
Between 2002 and 2018, his most popular works were:
- AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility (9267 citations)
- Adaptive Individuals In Evolving Populations: Models And Algorithms (186 citations)
- Systems and methods for creating and publishing relational data bases (86 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Cognitive science
His primary areas of study are Lead Finder, Baldwin effect, Inheritance, Genetic algorithm and Cognitive science.
The concepts of his Lead Finder study are interwoven with issues in Combinatorial chemistry, Searching the conformational space for docking and Molecular Docking Simulation.
His study in Baldwin effect intersects with areas of studies such as Selection, Natural selection, Adaptive behavior, Evolutionary ecology and Context.
His Human immunodeficiency virus research spans across into areas like Graphical user interface, AutoDock and Docking.
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