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Extending the Delaunay Triangulation Based Density Measurement to Many-Objective Optimization Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Detectable Genetic Algorithms-Based Techniques for Solving Dynamic Optimisation Problem with Unknown Active Variables

Authors : AbdelMonaem F. M. AbdAllah, Daryl L. Essam, Ruhul A. Sarker

Published in: Artificial Life and Computational Intelligence

Publisher: Springer International Publishing

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Abstract

A dynamic Optimisation Problem with Unknown Active Variables (DOPUAV) is a dynamic problem in which the activity of the variables changes as time passes, to simulate the dynamicity in the problem’s variables. In this paper, several variations of genetic algorithms are proposed to solve DOPUAV. They are called Detectable techniques. These techniques try to detect where the problem changes, before detecting the active variables. These variations are tested, then the best variation is compared with the best previously used algorithms namely Hyper Mutation (HyperM), Random Immigration GA (RIGA), as well as simple GA (SGA). The results and statistical analysis show the superiority of our proposed algorithm.

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Metadata
Title
Detectable Genetic Algorithms-Based Techniques for Solving Dynamic Optimisation Problem with Unknown Active Variables
Authors
AbdelMonaem F. M. AbdAllah
Daryl L. Essam
Ruhul A. Sarker
Copyright Year
2017
Book
Artificial Life and Computational Intelligence

Print ISBN: 978-3-319-51690-5

Electronic ISBN: 978-3-319-51691-2

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-51691-2_19

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