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Parameter Adaptive Differential Evolution
2009The performance of differential evolution is affected by its control parameters which in turn are dependent on the landscape characteristics of the objective function.As is clear from extensive experimental studies and theoretical analysis of simple spherical functions, inappropriate control parameter settings may lead to false or slow convergence and ...
Jingqiao Zhang, Arthur C. Sanderson
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Parameter Adaptive Control Systems
IFAC Proceedings Volumes, 1985Abstract The paper summarizes our experiences in the development of parameter adaptive control systems. First the basic and supplementary elements are discussed and practical requirements, like modularity, extensibility, access to intermediate results, etc. are stated. Then the parameter estimation methods are discussed.
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1996
Control over parameter passing is a key issue in distributed object-oriented applications. The two simplest solutions — passing objects by global reference and passing objects by deep copy — both have significant drawbacks. Instead, an intermediate amount of copying is often best.
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Control over parameter passing is a key issue in distributed object-oriented applications. The two simplest solutions — passing objects by global reference and passing objects by deep copy — both have significant drawbacks. Instead, an intermediate amount of copying is often best.
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Parameter-adaptive Controllers
1981This chapter treats parameter-adaptive controllers which are based on suitable parameter estimation methods, controller design methods and control algorithms, c.f. chapter 23. The relevant parameter estimation methods were discussed in chapter 24 and 25.
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Gaussian adaptation based parameter adaptation for differential evolution
2014 IEEE Congress on Evolutionary Computation (CEC), 2014Differential Evolution (DE), a global optimization algorithm based on the concepts of Darwinian evolution, is popular for its simplicity and effectiveness in solving numerous real-world optimization problems in real-valued spaces. The effectiveness of DE is due to the differential mutation operator that allows DE to automatically adjust between the ...
R. Mallipeddi +3 more
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Parameter-Efficient Adaptation for Computational Imaging
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)Deep learning-based methods provide remarkable performance in a number of computational imaging problems. Examples include end-to-end trained networks that map measurements to unknown signals, plug-and-play (PnP) methods that use pretrained denoisers as image prior, and model-based unrolled networks that train artifact removal blocks.
Nebiyou Yismaw +2 more
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Parameter Adaptation within Co-adaptive Learning Classifier Systems
2004The authors propose a co-adaptive approach to controlling parameters for coevolution-based learning classifier systems. By taking advantage of the on-line incremental learning capability of such systems, solutions can be produced that completely cover a target problem.
Chung-Yuan Huang, Chuen-Tsai Sun
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Genetic adaptation of segmentation parameters
2008This work presents a method for the automatic adaptation of segmentation parameters based on Genetic Algorithms. An intuitive and computationally simple fitness function, which expresses the similarity between the segmentation result and a reference provided by the user, is proposed.
G.A.O.P. Costa +3 more
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