Corus, D., Oliveto, P.S. and Yazdani, D. (2018) Artificial Immune Systems can find arbitrarily good approximations for the NP-Hard partition problem. In: Auger, A., Fonseca, C., Lourenço, N., Machado , P., Paquete, L. and Whitley, D., (eds.) Parallel Problem Solving from Nature – PPSN XV, PT II. PPSN: 15th International Conference on Parallel Problem Solving from Nature, 08-12 Sep 2018, Coimbra, Portugal. Lecture Notes in Computer Science, 11102 . Springer, Cham , pp. 16-28. ISBN 9783319992587
Abstract
Typical Artificial Immune System (AIS) operators such as hypermutations with mutation potential and ageing allow to efficiently overcome local optima from which Evolutionary Algorithms (EAs) struggle to escape. Such behaviour has been shown for artificial example functions such as Jump, Cliff or Trap constructed especially to show difficulties that EAs may encounter during the optimisation process. However, no evidence is available indicating that similar effects may also occur in more realistic problems. In this paper we perform an analysis for the standard NP-Hard Partition problem from combinatorial optimisation and rigorously show that hypermutations and ageing allow AISs to efficiently escape from local optima where standard EAs require exponential time. As a result we prove that while EAs and Random Local Search may get trapped on 4/3 approximations, AISs find arbitrarily good approximate solutions of ratio ( 1+ϵ ) for any constant ϵ within a time that is polynomial in the problem size and exponential only in 1/ϵ .
Metadata
Item Type: | Proceedings Paper |
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Copyright, Publisher and Additional Information: | © 2018 Springer Nature Switzerland AG. This is an author-produced version of a paper subsequently published in Auger A., Fonseca C., Lourenço N., Machado P., Paquete L., Whitley D. (eds) Parallel Problem Solving from Nature – PPSN XV. PPSN 2018. Lecture Notes in Computer Science, vol 11102. Uploaded in accordance with the publisher's self-archiving policy. |
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 ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL (EPSRC) EP/M004252/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 23 May 2019 12:02 |
Last Modified: | 23 May 2019 12:08 |
Status: | Published |
Publisher: | Springer, Cham |
Series Name: | Lecture Notes in Computer Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-319-99259-4_2 |
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Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:146352 |