An investigation of tuning a memetic algorithm for cross-domain search

Gumus, Duriye Betul; Özcan, Ender; Atkin, Jason

Abstract

Memetic algorithms, which hybridise evolutionary algorithms with local search, are well-known metaheuristics for solving combinatorial optimisation problems. A common issue with the application of a memetic algorithm is determining the best initial setting for the algorithmic parameters, but these can greatly influence its overall performance. Unlike traditional studies where parameters are tuned for a particular problem domain, in this study we do tuning that is applicable to cross-domain search. We extend previous work by tuning the parameters of a steady state memetic algorithm via a ‘design of experiments’ approach and provide surprising empirical results across nine problem domains, using a cross-domain heuristic search tool, namely HyFlex. The parameter tuning results show that tuning has value for cross-domain search. As a side gain, the results suggest that the crossover operators should not be used and, more interestingly, that single point based search should be preferred over a population based search, turning the overall approach into an iterated local search algorithm. The use of the improved parameter settings greatly enhanced the cross-domain performance of the algorithm, converting it from a poor performer in previous work to one of the stronger competitors.

Citation

Gumus, D. B., Özcan, E., & Atkin, J. (2016, July). An investigation of tuning a memetic algorithm for cross-domain search. Presented at 2016 IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada

• Presentation Conference Type: Edited Proceedings
• Conference Name: 2016 IEEE Congress on Evolutionary Computation
• Start Date: Jul 24, 2016
• End Date: Jul 29, 2016
• Acceptance Date: Mar 16, 2016
• Online Publication Date: Nov 21, 2016
• Publication Date: 2016
• Deposit Date: Aug 31, 2016
• Publicly Available Date: Nov 21, 2016
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Pages: 135-142
• Book Title: 2016 IEEE Congress on Evolutionary Computation (CEC): 24-29 July 2016 Vancouver, Canada
• ISBN: 978-1-5090-0624-3
• DOI: https://doi.org/10.1109/CEC.2016.7743788
• Keywords: Tuning; Memetics; Steady-state; Algorithm design and analysis; Statistics
• Public URL: https://nottingham-repository.worktribe.com/output/798442
• Publisher URL: https://ieeexplore.ieee.org/document/7743788/
• Additional Information: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
• Contract Date: Aug 31, 2016