Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/109119
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Type: Conference paper
Title: A fixed budget analysis of randomized search heuristics for the traveling salesperson problem
Author: Nallaperuma, S.
Neumann, F.
Sudholt, D.
Citation: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, 2014 / Igel, C. (ed./s), pp.807-814
Publisher: Association for Ccomputing Machinery
Issue Date: 2014
ISBN: 9781450326629
Conference Name: 2014 Annual Conference on Genetic and Evolutionary Computation (GECCO'14) (12 Jul 2014 - 16 Jul 2014 : Vancouver, Canada)
Statement of
Responsibility: 
Samadhi Nallaperuma, Frank Neumann, Dirk Sudholt
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 first fixed budget runtime analysis for a 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 budget.
Keywords: Traveling salesperson problem, fitness gain, theory
Rights: Copyright 2014 ACM. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full cita- tion on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or re- publish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.
RMID: 0030013663
DOI: 10.1145/2576768.2598302
Grant ID: http://purl.org/au-research/grants/arc/DP130104395
http://purl.org/au-research/grants/arc/DP140103400
Published version: http://dl.acm.org/citation.cfm?id=2576768
Appears in Collections:Computer Science publications

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