Projects per year
In this paper, we rigorously analyse how the magnitude and frequency of change may affect the performance of the algorithm (1+1) EAdyn on a set of artificially designed pseudo-Boolean functions, given a simple but well-defined dynamic framework. We demonstrate some counter-intuitive scenarios that allow us to gain a better understanding of how the dynamics of a function may affect the runtime of an algorithm. In particular, we present the function Magnitude, where the time it takes for the (1+1) EAdyn to relocate the global optimum is less than n2log n (i.e., efficient) with overwhelming probability if the magnitude of change is large. For small changes of magnitude, on the other hand, the expected time to relocate the global optimum is eΩ(n) (i.e., highly inefficient). Similarly, the expected runtime of the (1+1) EAdyn on the function Balance is O(n2) (efficient) for a high frequencies of change and nΩ(√n) (highly inefficient) for low frequencies of change. These results contribute towards a better understanding of dynamic optimisation problems in general and show how traditional analytical methods may be applied in the dynamic case.
|Title of host publication||Proceedings of the 11th Annual conference on Genetic and evolutionary computation|
|Number of pages||8|
|Publication status||Published - 12 Jul 2009|
|Event||Annual Conference on Genetic and Evolutionary Computation (GECCO '09), 11th - New York, United States|
Duration: 12 Jul 2009 → …
|Conference||Annual Conference on Genetic and Evolutionary Computation (GECCO '09), 11th|
|Period||12/07/09 → …|
FingerprintDive into the research topics of 'Dynamic evolutionary optimisation: an analysis of frequency and magnitude of change'. Together they form a unique fingerprint.
- 2 Finished
1/12/07 → 31/05/11
Project: Research Councils