Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/120107
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Type: Conference paper
Title: On the performance of baseline evolutionary algorithms on the dynamic knapsack problem
Author: Roostapour, V.
Neumann, A.
Neumann, F.
Citation: Parallel Problem Solving from Nature - PPSN XV: 15th International Conference, Coimbra, Portugal, September 8-12, 2018, Proceedings, Part I, 2018 / Auger, A., Fonseca, C.M., Lourenço, N., Machado, P., Paquete, L., Whitley, D. (ed./s), vol.11101 LNCS, pp.158-169
Publisher: Springer
Publisher Place: Cham
Issue Date: 2018
Series/Report no.: Lecture Notes in Computer Science; 11101
ISBN: 9783319992525
ISSN: 0302-9743
1611-3349
Conference Name: International Conference on Parallel Problem Solving from Nature (PPSN) (08 Sep 2018 - 12 Sep 2018 : Coimbra, Portugal)
Statement of
Responsibility: 
Vahid Roostapour, Aneta Neumann, and Frank Neumann
Abstract: Evolutionary algorithms are bio-inspired algorithms that can easily adapt to changing environments. In this paper, we study single- and multi-objective baseline evolutionary algorithms for the classical knapsack problem where the capacity of the knapsack varies over time. We establish different benchmark scenarios where the capacity changes every τ iterations according to a uniform or normal distribution. Our experimental investigations analyze the behavior of our algorithms in terms of the magnitude of changes determined by parameters of the chosen distribution, the frequency determined by τ and the class of knapsack instance under consideration. Our results show that the multi-objective approaches using a population that caters for dynamic changes have a clear advantage on many benchmarks scenarios when the frequency of changes is not too high.
Rights: © Springer Nature Switzerland AG 2018
RMID: 0030096911
DOI: 10.1007/978-3-319-99253-2_13
Grant ID: http://purl.org/au-research/grants/arc/DP160102401
Published version: https://doi.org/10.1007/978-3-319-99253-2
Appears in Collections:Computer Science publications

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