His scientific interests lie mostly in Mathematical optimization, Multi-objective optimization, Evolutionary algorithm, Evolutionary computation and Pareto principle. His study looks at the intersection of Mathematical optimization and topics like Algorithm with Spanning tree. His research integrates issues of Ranking, Optimization problem, Local search and Selection in his study of Multi-objective optimization.
As a part of the same scientific family, he mostly works in the field of Local search, focusing on Evolution strategy and, on occasion, Evolutionary art. His studies deal with areas such as Range, Theoretical computer science and Search algorithm as well as Evolutionary algorithm. Evolutionary computation is the subject of his research, which falls under Artificial intelligence.
His primary scientific interests are in Mathematical optimization, Artificial intelligence, Evolutionary algorithm, Evolutionary computation and Machine learning. The Mathematical optimization study which covers Algorithm that intersects with Spanning tree. His Artificial intelligence research includes themes of History matching, Task and Pattern recognition.
His Evolutionary algorithm study combines topics in areas such as Theoretical computer science and Management science. His Evolutionary computation study incorporates themes from Artificial neural network and Bioinformatics. Within one scientific family, David Corne focuses on topics pertaining to Pareto principle under Multi-objective optimization, and may sometimes address concerns connected to Evolution strategy.
David Corne spends much of his time researching Artificial intelligence, Evolutionary algorithm, Mathematical optimization, Machine learning and Context. His Artificial intelligence research incorporates themes from History matching, Cognition and Pattern recognition. His Evolutionary algorithm study integrates concerns from other disciplines, such as Optimization problem, Genetic programming and Profit.
His work on Evolutionary computation, Genetic algorithm, Flow network and Differential evolution as part of general Mathematical optimization research is frequently linked to Quality, bridging the gap between disciplines. His Evolutionary computation research is multidisciplinary, incorporating perspectives in Multi-swarm optimization, Probabilistic-based design optimization, Metaheuristic and Engineering optimization. His Machine learning research integrates issues from Latent Dirichlet allocation, Topic model, Training and Frequentist inference.
Artificial intelligence, Mathematical optimization, Topic model, Metaheuristic and Process are his primary areas of study. His biological study spans a wide range of topics, including History matching and Pattern recognition. His study in the field of Evolutionary computation also crosses realms of Quality.
His studies in Topic model integrate themes in fields like Visualization, Phrase and Data science. His work deals with themes such as Evolutionary algorithm, Multi-objective optimization, Graph theory and Shortest path problem, which intersect with Metaheuristic. The various areas that David Corne examines in his Evolutionary algorithm study include Sensitivity analysis, Uncertainty analysis and Genetic programming.
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.
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Joshua D. Knowles;David W. Corne.
Evolutionary Computation (2000)
The Pareto archived evolution strategy: a new baseline algorithm for Pareto multiobjective optimisation
J. Knowles;D. Corne.
congress on evolutionary computation (1999)
New Ideas In Optimization
David Corne;Marco Dorigo;Fred Glover;Dipankar Dasgupta.
The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation
David Corne;Joshua D. Knowles;Martin J. Oates.
parallel problem solving from nature (2000)
PESA-II: region-based selection in evolutionary multiobjective optimization
David W. Corne;Nick R. Jerram;Joshua D. Knowles;Martin J. Oates.
genetic and evolutionary computation conference (2001)
On metrics for comparing nondominated sets
J. Knowles;D. Corne.
congress on evolutionary computation (2002)
CREATIVE EVOLUTIONARY SYSTEMS
Peter J. Bentley;David W. Corne.
M-PAES: a memetic algorithm for multiobjective optimization
J.D. Knowles;D.W. Corne.
congress on evolutionary computation (2000)
Reducing Local Optima in Single-Objective Problems by Multi-objectivization
Joshua D. Knowles;Richard A. Watson;David Corne.
international conference on evolutionary multi criterion optimization (2001)
Properties of an adaptive archiving algorithm for storing nondominated vectors
J. Knowles;D. Corne.
IEEE Transactions on Evolutionary Computation (2003)
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