Abstract
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.
Original language | English |
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Title of host publication | Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2014) |
Place of Publication | New York |
Publisher | Association for Computing Machinery |
Pages | 975-982 |
ISBN (Print) | 978-1-4503-2662-9 |
DOIs | |
Publication status | Published - 2014 |
Event | GECCO '14 Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation - Vancouver, BC, Canada Duration: 12 Jul 2014 → 16 Jul 2014 |
Conference
Conference | GECCO '14 Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation |
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Country/Territory | Canada |
Period | 12 Jul 2014 → 16 Jul 2014 |