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Abstract: . Evolutionary programming (EP) has been widely used in numerical optimization in recent years. The adaptive parameters, also named step size control, in EP play a significant role which controls the step size of the objective variables in the evolutionary process. However, the step size control may not work in some cases. They are frequently lost and then make the search stagnate early. Applying the lower bound can maintain the step size in a work range, but it also constrains the objective... (Update)
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...lower bound is problemdependent. They also proposed the dynamical control of the lower bound as one way of overcoming this brittleness[6]. In this paper, another approach to avoiding this difficulty in EP is proposed. Our motivation is simple: Since natural selection cannot...
...of careful setting of the lower bound. They also proposed the dynamic control based on a cri terion similar to the 1 5 success rule [7]. This approach was shown to be effective for various problems. 4. Robust ES When ES is applied to an optimization problem successfully, it...
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2: Evolutionary Computation: Toward a New Philosophy of Machine Intelligence (context) - Fogel - 1995
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BibTeX entry: (Update)
K.-H. Liang, X. Yao and C. Newton (1998), "Dynamic Control of Adaptive Parameters in Evolutionary Programming ", Proc. of the Second Asia-Pacific Conference on Simulated Evolution and Learning, SpringerVerlag. http://citeseer.ist.psu.edu/article/liang98dynamic.html More
@article{ liang99dynamic,
author = "Ko-Hsin Liang and Xin Yao and Charles Newton",
title = "Dynamic Control of Adaptive Parameters in Evolutionary Programming",
journal = "Lecture Notes in Computer Science",
volume = "1585",
pages = "42--49",
year = "1999",
url = "citeseer.ist.psu.edu/article/liang98dynamic.html" }
Citations (may not include all citations):1749 An Introduction to Probability Theory and Its Applications (context) - Feller - 1968
227 Evolution and Optimum Seeking (context) - Schwefel - 1995
157 An overview of evolutionary algorithms for parameter optimiz.. (context) - Back, Schwefel - 1993
48 Toward a theory of evolution strategies: Self-adaptation - Beyer - 1995
38 Applying evolutionary programming to selected control proble.. (context) - Fogel - 1994
35 Adaptive and self-adaptive evolutionary computation - Angeline - 1995
30 Fast evolutionary programming - Yao, Liu - 1996
30 Meta-evolutionary programming (context) - Fogel, Fogel et al. - 1991
21 Evolutionary Algorithms in Theory and Practice: Evolution St.. (context) - Back - 1996
17 Introduction to Probability Theory and statistical Inference (context) - Larson - 1982
6 A comparison of evolutionary programming and genetic algorit.. (context) - Fogel - 1995
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