Abstract
The field of evolutionary computation has traditionally focused on static optimisation problems but recently, many new approaches have been proposed that adapt traditional evolutionary algorithms to deal with the task of tracking high-quality solutions as the search space changes over time. Algorithms developed specifically for dynamic domains have been tested on a wide range of different problems, including well-specified benchmark generators. However, the lack of theoretical results, a general omission of references to actual real-world scenarios, as well as a substantial emphasis on the continuous domain may divert attention away from some highly relevant issues. Here we review the state of the field and analyse dynamics in the combinatorial domain, using the subset sum problem as an example. It is shown that some of the assumptions underlying the development of new algorithms do not necessarily hold in the case of discrete optimisation. Furthermore, it is argued that more attention should be paid to the underlying dynamics and the impact of the representation used.
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Rohlfshagen, P., Yao, X. (2008). Attributes of Dynamic Combinatorial Optimisation. In: Li, X., et al. Simulated Evolution and Learning. SEAL 2008. Lecture Notes in Computer Science, vol 5361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89694-4_45
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DOI: https://doi.org/10.1007/978-3-540-89694-4_45
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