English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Dynamic algorithm selection aims to exploit the complementarity of multipleoptimization algorithms by switching between them during the search. Whilethese kinds of dynamic algorithms have been shown to have potential tooutperform their component algorithms, it is still unclear how this potentialcan best be realized. One promising approach is to make use of landscapefeatures to enable a per-run trajectory-based switch. Here, the samples seen bythe first algorithm are used to create a set of features which describe thelandscape from the perspective of the algorithm. These features are then usedto predict what algorithm to switch to.  In this work, we extend this per-run trajectory-based approach to consider awide variety of potential points at which to perform the switch. We show thatusing a sliding window to capture the local landscape features containsinformation which can be used to predict whether a switch at that point wouldbe beneficial to future performance. By analyzing the resulting models, weidentify what features are most important to these predictions. Finally, byevaluating the importance of features and comparing these values betweenmultiple algorithms, we show clear differences in the way the second algorithminteracts with the local landscape features found before the switch.

To Switch or not to Switch: Predicting the Benefit of Switching between Algorithms based on Trajectory Features