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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Model-based evolutionary algorithms (EAs) adapt an underlying search model tofeatures of the problem at hand, such as the linkage between problem variables.The performance of EAs often deteriorates as multiple modes in the fitnesslandscape are modelled with a unimodal search model. The number of modes ishowever often unknown a priori, especially in a black-box setting, whichcomplicates adaptation of the search model. In this work, we focus on modelsthat can adapt to the multi-modality of the fitness landscape. Specifically, weintroduce Hill-Valley Clustering, a remarkably simple approach to adaptivelycluster the search space in niches, such that a single mode resides in eachniche. In each of the located niches, a core search algorithm is initialized tooptimize that niche. Combined with an EA and a restart scheme, the resultingHill-Valley EA (HillVallEA) is compared to current state-of-the-art nichingmethods on a standard benchmark suite for multi-modal optimization. Numericalresults in terms of the detected number of global optima show that, in spite ofits simplicity, HillVallEA is competitive within the limited budget of thebenchmark suite, and shows superior performance in the long run.

Real-Valued Evolutionary Multi-Modal Optimization driven by Hill-Valley Clustering