Papers by Hans-Georg Beyer
eldorado.tu-dortmund.de
Page 1. Evolution are Algorithmen { Begri e und De nitionen Hans-Georg Beyer 1) , Eva Brucherseif... more Page 1. Evolution are Algorithmen { Begri e und De nitionen Hans-Georg Beyer 1) , Eva Brucherseifer 2) , Wilfried Jakob 3) , Hartmut Pohlheim 4) , Bernhard Sendho 5) und Thanh Binh To 6) 1)Universit at Dortmund, Informatik ...
Theoretical Computer Science, 2016
Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference - GECCO '12, 2012
ABSTRACT This paper investigates mutation strength control using Meta-ES on the sharp ridge. The ... more ABSTRACT This paper investigates mutation strength control using Meta-ES on the sharp ridge. The asymptotical analysis presented allows for the prediction of the dynamics in ridge as well as in radial direction. Being based on this analysis the problem of the choice of population size lambda and isolation parameter gamma will be tackled. Remarkably, the qualitative convergence behavior is not determined by gamma alone, but rather by the number of function evaluations lambda gamma devoted to the inner ES.
Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, 2008
This paper considers self-adaptive (µ/µI , λ)-evolution strategies on the noisy sharp ridge. The ... more This paper considers self-adaptive (µ/µI , λ)-evolution strategies on the noisy sharp ridge. The evolution strategy (ES) is treated as a dynamical system using the so-called evolution equations to model the ES's behavior. The approach requires the determination of the one-generational expected changes of the state variables -the progress measures. For the analysis, the stationary state behavior of the ES on the sharp ridge is considered. Contrary to the usual perception of noise, it is shown that noise has a positive influence on the performance. An explanation for this astonishing behavior is given and conditions for the usefulness of noise in other fitness landscapes are discussed.
Parallel Problem Solving from Nature, 2004
Genetic Algorithms and Evolutionary Computation, 2002
Evolution strategies are general, nature-inspired heuristics for search and optimization. Support... more Evolution strategies are general, nature-inspired heuristics for search and optimization. Supported both by empirical evidence and by recent theoretical findings, there is a common belief that evolution strategies are robust and reliable, and frequently they are the method of choice if neither derivatives of the objective function are at hand nor differentiability and numerical accuracy can be assumed. However, despite their widespread use, there is little exchange between members of the "classical" optimization community and people working in the field of evolutionary computation. It is our belief that both sides would benefit from such an exchange.
This article gives a comprehensive introduction into one of the main branches of evolutionary com... more This article gives a comprehensive introduction into one of the main branches of evolutionary computation - the evolution strategies (ES) the history of which dates back to the 1960s in Germany. Starting from a survey of history the philosophical background is explained in order to make understandable why ES are realized in the way they are. Basic ES algorithms and
Proceedings of the 11th Annual conference on Genetic and evolutionary computation - GECCO '09, 2009
This paper introduces simple control rules for the mutation strength and the parental population ... more This paper introduces simple control rules for the mutation strength and the parental population size using the Meta-ES approach. An in-depth analysis is presented on the mutation strength control using the sphere model. A heuristic formula for the outer mutation parameter will be proposed based on the theoretical analysis. Finally, a new evolutionary control strategy for the parental population size
Lecture Notes in Computer Science, 1997
Lecture Notes in Computer Science, 2003
Lecture Notes in Computer Science, 2000
Noise is present in many optimization problems. Evolutionary algorithms are frequently reported t... more Noise is present in many optimization problems. Evolutionary algorithms are frequently reported to be robust with regard to the effects of noise. In many cases, there is a tradeoff between the accuracy with which the fitness of a candidate solution is determined and the number of candidate solutions that are evaluated in every time step. This paper addresses this tradeoff
Lecture Notes in Computer Science, 2004
ABSTRACT In optimization tasks that deal with real-world applications noise is very common leadin... more ABSTRACT In optimization tasks that deal with real-world applications noise is very common leading to degradation of the performance of Evolution Strategies. We will consider the quality gain of an (1,λ)-ES under noisy fitness evaluations for arbitrary fitness functions. The equation developed will be applied to several test functions to check its predictive quality.
Lecture Notes in Computer Science, 2006
This paper presents first results of an analysis of the σ-self-adaptation mechanism on the sharp ... more This paper presents first results of an analysis of the σ-self-adaptation mechanism on the sharp ridge for non-recombinative (1, λ) evolution strategies (ES). To analyze the ES's evolution, we consider the so-called evolution equations which describe the one-generation change. Neglecting stochastic perturbations and considering only the mean value dynamics, we will investigate possible causes why self-adaptation can fail on the sharp ridge.
Natural Computing Series, 2001
Lecture Notes in Computer Science, 1994
Lecture Notes in Computer Science, 1997
An analysis of the dynamic behavior of Evolution Strategies applied to Traveling Salesman Problem... more An analysis of the dynamic behavior of Evolution Strategies applied to Traveling Salesman Problems is presented. For a special class of Traveling Salesman Problems a stochastic model of the optimization process is introduced. Based on this model di erent features determining the optimization process of Evolution Strategies are analyzed. In addition the stochastic model is extended to explain some aspects of Simulated Annealing.
Lecture Notes in Computer Science, 1996
... As in the (#/#z, A) case as well as for (#, A)-strategies the new parents are produced by (#,... more ... As in the (#/#z, A) case as well as for (#, A)-strategies the new parents are produced by (#, A)-selection, sometimes called truncation selection, ie the # best offspring are chosen. 2 The Asymptotic'Progress Law of the (#/#,A)-ES ... 2.3 The Asymptotic Progress Law ...
Natural Computing Series, 2001
Natural Computing Series, 2001
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Papers by Hans-Georg Beyer