Next Article in Journal
Spherical Fuzzy Graphs with Application to Decision-Making
Previous Article in Journal
Acknowledgement to Reviewers of MCA in 2019
Previous Article in Special Issue
Non-Epsilon Dominated Evolutionary Algorithm for the Set of Approximate Solutions
Open AccessArticle

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

1
Department of Computer Science in Jamoum, Umm Al-Qura University, Makkah 25371, Saudi Arabia
2
Department of Computer Science, Faculty of Computers & Information, Assiut University, Assiut 71526, Egypt
3
Computers and Systems Engineering Department, Mansoura University, Mansoura 35516, Egypt
4
Department of Computer Sciences, College of Computing and Information Technology, University of Jeddah, Jeddah 23218, Saudi Arabia
5
Department of Electrical Engineering, Computers and Systems Section, Faculty of Engineering, Aswan University, Aswan 81528, Egypt
*
Author to whom correspondence should be addressed.
Math. Comput. Appl. 2020, 25(1), 7; https://doi.org/10.3390/mca25010007
Received: 26 November 2019 / Revised: 9 January 2020 / Accepted: 14 January 2020 / Published: 16 January 2020
(This article belongs to the Special Issue Numerical and Evolutionary Optimization 2019)
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.
Keywords: evolutionary algorithms; genetic algorithm; evolution strategies; regression; neural networks; quadratic coding; quadratic search evolutionary algorithms; genetic algorithm; evolution strategies; regression; neural networks; quadratic coding; quadratic search
MDPI and ACS Style

Hedar, A.-R.; Deabes, W.; Almaraashi, M.; Amin, H.H. Evolutionary Algorithms Enhanced with Quadratic Coding and Sensing Search for Global Optimization. Math. Comput. Appl. 2020, 25, 7.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop