2008 - Evolutionary Computation Pioneer Award, IEEE Computational Intelligence Society
2007 - Meritorious Service Award, IEEE Computational Intelligence Society
2004 - IEEE Kiyo Tomiyasu Award “For outstanding contributions to the science and technology of computational intelligence and to the development and expansion of that field.”
1999 - IEEE Fellow For contributions to the scientific advancement of evolutionary computation.
His primary areas of investigation include Artificial intelligence, Evolutionary computation, Evolutionary programming, Evolutionary algorithm and Artificial neural network. His Artificial intelligence study combines topics in areas such as Iterated function and Machine learning. The Evolutionary computation study combines topics in areas such as Theoretical computer science, Imperialist competitive algorithm, Field, Genetic algorithm and Data science.
His Evolutionary programming study incorporates themes from Algorithm and Genetic programming, Genetic representation. His Evolutionary algorithm study combines topics from a wide range of disciplines, such as Simulated annealing, Tabu search, Branch and bound, Local search and Heuristic. His Artificial neural network research is multidisciplinary, incorporating perspectives in Discrete mathematics, Global optimum, Local optimum and Parallel processing.
David B. Fogel mainly focuses on Artificial intelligence, Evolutionary computation, Evolutionary programming, Evolutionary algorithm and Artificial neural network. In the subject of general Artificial intelligence, his work in Evolutionary acquisition of neural topologies, Computational intelligence and Pattern recognition is often linked to Process, thereby combining diverse domains of study. In his study, Programming language is strongly linked to Genetic programming, which falls under the umbrella field of Evolutionary computation.
His biological study spans a wide range of topics, including Evolution strategy and Genetic representation. His research investigates the connection between Evolutionary algorithm and topics such as Algorithm that intersect with problems in Set. David B. Fogel combines topics linked to Fuzzy control system with his work on Artificial neural network.
David B. Fogel mainly investigates Artificial intelligence, Evolutionary algorithm, Evolutionary computation, Artificial neural network and Evolutionary programming. In his work, Computer program and Prolog is strongly intertwined with Machine learning, which is a subfield of Artificial intelligence. David B. Fogel has researched Evolutionary algorithm in several fields, including Entertainment, Evaluation function and Adaptation.
His studies in Evolutionary computation integrate themes in fields like Management science, Field, Human–computer interaction, Game theory and Software. His work investigates the relationship between Artificial neural network and topics such as Computational intelligence that intersect with problems in Fuzzy control system, Data science, World Wide Web, Tournament and Artificial Intelligence System. In his study, Learnable Evolution Model and Genetic representation is inextricably linked to Evolution strategy, which falls within the broad field of Evolutionary programming.
His primary scientific interests are in Artificial intelligence, Evolutionary computation, Evolutionary algorithm, Operations research and Outcome. His work in the fields of Artificial neural network and Robot overlaps with other areas such as Ant robotics. The study incorporates disciplines such as Intelligent control, Local optimum, Computational intelligence and Fuzzy control system in addition to Artificial neural network.
David B. Fogel is involved in the study of Evolutionary computation that focuses on Evolutionary music in particular. His study in Evolutionary acquisition of neural topologies, Human-based evolutionary computation, Evolutionary programming, Java Evolutionary Computation Toolkit and Interactive evolutionary computation is carried out as part of his studies in Evolutionary algorithm. His study focuses on the intersection of Operations research and fields such as Automatic testing with connections in the field of Heuristics.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Handbook of Evolutionary Computation
Thomas Back;David B. Fogel;Zbigniew Michalewicz.
(1997)
Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
David B. Fogel.
(1995)
How to Solve It: Modern Heuristics
Zbigniew Michalewicz;David B. Fogel.
(2004)
Artificial Intelligence through Simulated Evolution
David B. Fogel.
(1998)
An introduction to simulated evolutionary optimization
D.B. Fogel.
IEEE Transactions on Neural Networks (1994)
Evolutionary Computation 1 : Basic Algorithms and Operators
Thomas Baeck;D.B Fogel;Z Michalewicz.
(2000)
Evolutionary Computation 1
Thomas Bck;David B Fogel;Zbigniew Michalewicz.
(2000)
Evolutionary Computation 2
Thomas Bck;David B Fogel;Zbigniew Michalewicz.
(2000)
Rapid automated molecular replacement by evolutionary search
Charles R. Kissinger;Daniel K. Gehlhaar;David B. Fogel.
Acta Crystallographica Section D-biological Crystallography (1999)
Evolutionary Computation: The Fossil Record
David B. Fogel.
(1998)
Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.
If you think any of the details on this page are incorrect, let us know.
University of Adelaide
Amazon Web Services
University of Missouri
University of Illinois at Chicago
Leiden University
Université Libre de Bruxelles
University of Tokyo
Vorarlberg University of Applied Sciences
Michigan State University
Michigan State University
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: