His primary areas of investigation include Evolutionary algorithm, Evolutionary computation, Mathematical optimization, Genetic algorithm and Artificial intelligence. His study in the field of Human-based evolutionary computation also crosses realms of The Internet. His Evolutionary computation research integrates issues from Distributed algorithm, Management science and User requirements document.
His studies in Mathematical optimization integrate themes in fields like Constraint satisfaction, Algorithm design, Calibration and Relevance. His research in Genetic algorithm intersects with topics in Population size, Algorithm, Statistical significance, Crossover and Variable-order Markov model. He has researched Artificial intelligence in several fields, including Machine learning and Adaptation.
His primary areas of study are Artificial intelligence, Evolutionary algorithm, Robot, Evolutionary computation and Evolutionary robotics. His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Control theory. His Evolutionary algorithm research is multidisciplinary, incorporating elements of Distributed computing and Selection.
His study in the fields of Self-reconfiguring modular robot under the domain of Robot overlaps with other disciplines such as Process, Line and Modular design. His work deals with themes such as Theoretical computer science and Data science, which intersect with Evolutionary computation. A. E. Eiben interconnects Social learning and Robot learning in the investigation of issues within Evolutionary robotics.
A. E. Eiben spends much of his time researching Robot, Artificial intelligence, Evolutionary robotics, Self-reconfiguring modular robot and Control theory. His research integrates issues of Evolutionary algorithm, Distributed computing, Selection and Human–computer interaction in his study of Robot. While working on this project, A. E. Eiben studies both Evolutionary algorithm and Turing.
The Artificial intelligence study combines topics in areas such as Machine learning and Fitness function. His Evolutionary robotics research incorporates elements of Representation, Encoding, Component and Set. His study focuses on the intersection of Differential evolution and fields such as Estimation of distribution algorithm with connections in the field of Evolutionary computation.
A. E. Eiben mainly focuses on Robot, Artificial intelligence, Self-reconfiguring modular robot, Evolutionary robotics and Human–computer interaction. His Robot research includes elements of Evolutionary algorithm, Swarm behaviour and Actuator. His Evolutionary algorithm research includes themes of Bayesian optimization and Simulation.
Artificial intelligence is often connected to Fitness function in his work. The concepts of his Self-reconfiguring modular robot study are interwoven with issues in Space, Control theory, HyperNEAT and Set. The various areas that A. E. Eiben examines in his Evolutionary robotics study include Construct and Robotic systems.
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.
Introduction to evolutionary computing
Agoston E. Eiben;J. E. Smith.
Parameter control in evolutionary algorithms
A.E. Eiben;R. Hinterding;Z. Michalewicz.
IEEE Transactions on Evolutionary Computation (1999)
Parallel Problem Solving from Nature — PPSN V
Agoston E. Eiben;Thomas Bäck;Marc Schoenauer;Hans-Paul Schwefel.
Genetic algorithms with multi-parent recombination
A. E. Eiben;Paul-Erik Raué;Zsófia Ruttkay.
parallel problem solving from nature (1994)
Parameter tuning for configuring and analyzing evolutionary algorithms
A. E. Eiben;Selmar K. Smit.
Swarm and evolutionary computation (2011)
Parameter Control in Evolutionary Algorithms
A. E. Eiben;Zbigniew Michalewicz;Marc Schoenauer;James E. Smith.
Studies in computational intelligence (2007)
Evolutionary Programming VII
V. W. Porto;N. Saravanan;D. Waagen;A. E. Eiben.
Global Convergence of Genetic Algorithms: A Markov Chain Analysis
A. E. Eiben;Emile H. L. Aarts;Emile H. L. Aarts;Kees M. van Hee.
parallel problem solving from nature (1990)
Adaptation in evolutionary computation: a survey
R. Hinterding;Z. Michalewicz;A.E. Eiben.
ieee international conference on evolutionary computation (1997)
On evolutionary exploration and exploitation
A. E. Eiben;C. A. Schippers.
Fundamenta Informaticae (1998)
Profile was last updated on December 6th, 2021.
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