Nallaperuma, S., Neumann, F. and Sudholt, D. orcid.org/0000-0001-6020-1646 (2016) Expected Fitness Gains of Randomized Search Heuristics for the Traveling Salesperson Problem. Evolutionary Computation. ISSN 1063-6560
Abstract
Randomized search heuristics are frequently applied to NP-hard combinatorial optimization problems. The runtime analysis of randomized search heuristics has contributed tremendously to their theoretical understanding. Recently, randomized search heuristics have been examined regarding their achievable progress within a fixed time budget. We follow this approach and present a fixed budget analysis for an NP-hard combinatorial optimization problem. We consider the well-known Traveling Salesperson problem (TSP) and analyze the fitness increase that randomized search heuristics are able to achieve within a given fixed time budget. In particular, we analyze Manhattan and Euclidean TSP instances and Randomized Local Search (RLS), (1 + 1) EA and (1 + λ) EA algorithms for the TSP in a smoothed complexity setting and derive the lower bounds of the expected fitness gain for a specified number of generations.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 by the Massachusetts Institute of Technology. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Traveling Salesperson Problem; fitness gain; fixed-budget analysis; runtime analysis theory |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number EUROPEAN COMMISSION - FP6/FP7 SAGE - 138086 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 19 Dec 2016 16:03 |
Last Modified: | 28 Feb 2017 01:38 |
Published Version: | http://dx.doi.org/10.1162/EVCO_a_00199 |
Status: | Published |
Publisher: | Massachusetts Institute of Technology Press (MIT Press): STM Titles |
Refereed: | Yes |
Identification Number: | 10.1162/EVCO_a_00199 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:108744 |