His primary scientific interests are in Evolution strategy, Mathematical optimization, Evolutionary algorithm, Evolutionary computation and Genetic algorithm. His study in Evolution strategy is interdisciplinary in nature, drawing from both Hypersphere and Genetic representation. His work on Robust optimization as part of general Mathematical optimization research is frequently linked to Self adaptation, bridging the gap between disciplines.
His Evolutionary algorithm study combines topics from a wide range of disciplines, such as Algorithm and Noise. His work on Java Evolutionary Computation Toolkit as part of general Evolutionary computation research is often related to Selection, thus linking different fields of science. His work focuses on many connections between Genetic algorithm and other disciplines, such as Crossover, that overlap with his field of interest in Population variance, Fitness landscape, Genetic operator and Search algorithm.
Hans-Georg Beyer spends much of his time researching Mathematical optimization, Evolution strategy, Evolutionary algorithm, Noise and Evolutionary computation. His Mathematical optimization study frequently links to other fields, such as Adaptation. His research integrates issues of Algorithm, Limit and Applied mathematics in his study of Evolution strategy.
The Evolutionary algorithm study combines topics in areas such as Benchmarking, Benchmark, Constrained optimization and Search algorithm. His research investigates the connection between Noise and topics such as Gaussian noise that intersect with problems in Order statistic. His work carried out in the field of Evolutionary computation brings together such families of science as Theoretical computer science and Genetic programming.
The scientist’s investigation covers issues in Evolution strategy, Mathematical optimization, Constrained optimization, Covariance matrix and Projection. His Evolution strategy study incorporates themes from Lambda, Boundary and Truncation. His biological study spans a wide range of topics, including Upper and lower bounds and Robustness.
His Constrained optimization research is multidisciplinary, incorporating perspectives in Single objective, Constraint, Benchmark, Evolutionary algorithm and Function. Hans-Georg Beyer interconnects Benchmarking and Search algorithm in the investigation of issues within Evolutionary algorithm. His research in Linear programming intersects with topics in Evolutionary computation and Differential evolution.
His primary areas of investigation include Mathematical optimization, Evolution strategy, CMA-ES, Constrained optimization and Covariance matrix. Mathematical optimization connects with themes related to Resampling in his study. Hans-Georg Beyer has included themes like Truncation, Fitness landscape, Limit and Robustness in his Evolution strategy study.
As part of the same scientific family, he usually focuses on Constrained optimization, concentrating on Benchmark and intersecting with Search algorithm, Evolutionary algorithm, Benchmarking and Single objective. The various areas that Hans-Georg Beyer examines in his Linear programming study include Evolutionary computation, Differential evolution and Interior point method. His Optimization problem study combines topics in areas such as Sampling, Function and Computational intelligence.
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.
Evolution strategies –A comprehensive introduction
Hans-Georg Beyer;Hans-Paul Schwefel.
Natural Computing (2002)
Robust Optimization - A Comprehensive Survey
Hans-Georg Beyer;Bernhard Sendhoff.
Computer Methods in Applied Mechanics and Engineering (2007)
The Theory of Evolution Strategies
Hans Georg Beyer.
Genetic and Evolutionary Computation -- GECCO-2003
Erick Cantú-Paz;James A. Foster;Kalyanmoy Deb;Lawrence David Davis.
Self-Adaptive Genetic Algorithms with Simulated Binary Crossover
Kalyanmoy Deb;Hans-georg Beyer.
Evolutionary Computation (2001)
Parallel Problem Solving from Nature — PPSN VII
Juan Julián Merelo Guervós;Panagiotis Adamidis;Hans-Georg Beyer;Hans-Paul Schwefel.
On self-adaptive features in real-parameter evolutionary algorithms
H.-G. Beyer;K. Deb.
IEEE Transactions on Evolutionary Computation (2001)
Evolutionary algorithms in noisy environments : theoretical issues and guidelines for practice
Computer Methods in Applied Mechanics and Engineering (2000)
Parallel Problem Solving from Nature - PPSN IX
Thomas Philip Runarsson;Hans-Georg Beyer;Edmund Burke;Juan J. Merelo-Guervós.
Toward a theory of evolution strategies: Self-adaptation
Evolutionary Computation (1995)
If you think any of the details on this page are incorrect, let us know.
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: