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2017 | OriginalPaper | Chapter

A Meta-Genetic Algorithm for Hybridizing Metaheuristics

Authors : Ahmed Hassan, Nelishia Pillay

Published in: Progress in Artificial Intelligence

Publisher: Springer International Publishing

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Abstract

The research presented in this paper forms part of the initiative aimed at automating the design of intelligent techniques to make them more accessible to non-experts. This study focuses on automating the hybridization of metaheuristics and parameter tuning of the individual metaheuristics. It is an initial attempt at testing the feasibility to automate this design process. A genetic algorithm is used for this purpose. Each hybrid metaheuristic is a combination of metaheuristics and corresponding parameter values. The genetic algorithm explores the space of these combinations. The genetic algorithm is evaluated by applying it to solve the symmetric travelling salesman problem. The evolved hybrid metaheuristics are found to perform competitively with the manually designed hybrid approaches from previous studies and outperform the metaheuristics applied individually. The study has also revealed the potential reusability of the evolved hybrids. Based on the success of this initial study, different problem domains shall be used to verify the automation approach to the design of hybrid metaheuristics.

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Footnotes
2
Large-scale tsp solvers require special data structures and heuristics [17] which falls out of the scope of this study.
 
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Metadata
Title
A Meta-Genetic Algorithm for Hybridizing Metaheuristics
Authors
Ahmed Hassan
Nelishia Pillay
Copyright Year
2017
Book
Progress in Artificial Intelligence

Print ISBN: 978-3-319-65339-6

Electronic ISBN: 978-3-319-65340-2

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-65340-2_31

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