Robust Linear Neural Network for Constrained Quadratic Optimization

Liu, Zixin; Liu, Yuanan; Xiong, Lianglin

doi:10.1155/2017/5073640

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…These results, in better classification of the ANNs, use of the power of both methods, the learning ability of the ANNs, and the best parameter values of EA. On the other hand, little research, such as this one, do the opposite thing by optimizing the evolutionary algorithms using ANNs for various purposes [63][64][65].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Evolutionary Algorithms Enhanced with Quadratic Coding and Sensing Search for Global Optimization

Hedar

Deabes

Almaraashi

et al. 2020

MCA

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Enhancing Evolutionary Algorithms (EAs) using mathematical elements significantly contribute to their development and control the randomness they are experiencing. Moreover, the automation of the primary process steps of EAs is still one of the hardest problems. Specifically, EAs still have no robust automatic termination criteria. Moreover, the highly random behavior of some evolutionary operations should be controlled, and the methods should invoke advanced learning process and elements. As follows, this research focuses on the problem of automating and controlling the search process of EAs by using sensing and mathematical mechanisms. These mechanisms can provide the search process with the needed memories and conditions to adapt to the diversification and intensification opportunities. Moreover, a new quadratic coding and quadratic search operator are invoked to increase the local search improving possibilities. The suggested quadratic search operator uses both regression and Radial Basis Function (RBF) neural network models. Two evolutionary-based methods are proposed to evaluate the performance of the suggested enhancing elements using genetic algorithms and evolution strategies. Results show that for both the regression, RBFs and quadratic techniques could help in the approximation of high-dimensional functions with the use of a few adjustable parameters for each type of function. Moreover, the automatic termination criteria could allow the search process to stop appropriately.

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Section: Artificial Neural Networkmentioning

confidence: 99%