On the performance of baseline evolutionary algorithms on the dynamic knapsack problem
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(Accepted version)
Date
2018
Authors
Roostapour, V.
Neumann, A.
Neumann, F.
Editors
Auger, A.
Fonseca, C.M.
Lourenço, N.
Machado, P.
Paquete, L.
Whitley, L.D.
Fonseca, C.M.
Lourenço, N.
Machado, P.
Paquete, L.
Whitley, L.D.
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Conference paper
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Lecture Notes in Artificial Intelligence, 2018 / Auger, A., Fonseca, C.M., Lourenço, N., Machado, P., Paquete, L., Whitley, L.D. (ed./s), vol.11101, pp.158-169
Statement of Responsibility
Vahid Roostapour, Aneta Neumann, and Frank Neumann
Conference Name
International Conference on Parallel Problem Solving from Nature (PPSN) (8 Sep 2018 - 12 Sep 2018 : Coimbra, Portugal)
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.
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© Springer Nature Switzerland AG 2018