The Theory of Evolution Strategies

By Hans-Georg Beyer. Natural Computing Series, 2001

642 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/62d9c9469bd8f99548ba296a0db72a6752d6d181

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In Artificial Intelligence

Citations in the corpus

Covariance Matrix Adaptation for Multi-objective Optimization (2007, background): “…are derived from the precursor algorithms and validated by sketchy simulations on simple test functions: the target success rate is close to the well-known 1 / 5 and depends on λ , because the optimal success rate in the (1 + λ ) -ES certainly decreases with increasing λ (Beyer, 2001, p. 78f).”
Stochastic Optimization: a Review (2002, background): “Bäck (1996) presents an excellent overview of GA, and Beyer (2001) and Vose (1999) offer recent thoughts on the theory underlying the method.”
Structural model updating and prediction variability using Pareto optimal models (2008, background): “Evolution strategies [31] are more appropriate and effective to use in such cases.”
Structural identification based on optimally weighted modal residuals (2007, background): “Evolution strategies [34] are more appropriate and effective to use in such cases.”
Multicriteria Optimization and Decision Making: Principles, Algorithms and Case Studies (2024, positive): “On simple quadratic functions, Beyer [9] derived expressions showing a linear convergence rate of ES with optimally adapted step sizes, using a (μ + λ) or”
Optimization of Convex Functions with Random Pursuit (2011, background): “Notable exceptions can be found in the literature on adaptive step size random search (also known as Evolution Strategies) [3, 11], on Markov chain methods for volume estimation, rounding, and optimization [33], and in Nesterov’s recent work on complexity bounds for gradient-free convex optimization”
Sequential parameter optimization (2005, background): “Results from this case study support results presented in Beyer (2001) .”
Darwin or Lamarck? Future Challenges in Evolutionary Algorithms for Knowledge Discovery and Data Mining (2014, background): “(2001) [44] proposed an extended ES called Robust Evolution Strategy (RES), which has redundant neutral strategy parameters and which adopts new mutation mechanisms in order to utilize selectively neutral mutations to improve the adaptability of strategy parameters, a similar approach was proposed i”
Recent approaches to global optimization problems through Particle Swarm Optimization (2002, neutral): “To study the behavior of the above methods Arnold considered a simple n-dimensional sphere model (see also (Beyer, 2000; Beyer, 2001)) and extensively tested these methods in the presence of additive Gaussian noise.”
Modern Evolution Strategies for Creativity: Fitting Concrete Images and Abstract Concepts (2021, methodology): “Evolution Strategies (ES) [3, 4] has been applied to optimization problems for a long period of time.”
Critical Hyper-Parameters: No Random, No Cry (2017, methodology): “Published methods for hyper-parameters search include evolution strategies [2], Gaussian processes [10, 23], pattern search [4], grid sampling and random sampling [1].”
Evolution Strategies (2018, background): “For ﬁxed truncation ratio µ/λ , c µ/µ,λ approaches a ﬁnite limit value as λ and µ increase [15, 8].”