Evolutionary algorithms and artificial immune systems on a bi-stable dynamic optimisation problem

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddTrafodion Cynhadledd (Nid-Cyfnodolyn fathau)

20 Dyfyniadau(SciVal)

Crynodeb

Dynamic optimisation is an important area of application for evolutionary algorithms and other randomised search heuristics. Theoretical investigations are currently far behind practical successes. Addressing this deficiency a bi-stable dynamic optimisation problem is introduced and the performance of standard evolutionary algorithms and artificial immune systems is assessed. Deviating from the common theoretical perspective that concentrates on the expected time to find a global optimum (again) here the ‘any time performance’ of the algorithms is analysed, i.e., the expected function value at each step. Basis for the analysis is the recently introduced perspective of fixed budget computations. Different dynamic scenarios are considered which are characterised by the length of the stable phases. For each scenario different population sizes are examined. It is shown that the evolutionary algorithms tend to have superior performance in almost all cases.
Iaith wreiddiolSaesneg
TeitlGECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference
Man cyhoeddiNew York
CyhoeddwrAssociation for Computing Machinery
Tudalennau975-982
Nifer y tudalennau8
ISBN (Argraffiad)978-1-4503-2662-9
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 2014
DigwyddiadGECCO '14 Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation - Vancouver, BC, Canada
Hyd: 12 Gorff 201416 Gorff 2014

Cyfres gyhoeddiadau

EnwGECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference

Cynhadledd

CynhadleddGECCO '14 Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation
Gwlad/TiriogaethCanada
Cyfnod12 Gorff 201416 Gorff 2014

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