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

We present a new method for analyzing the running time of parallelevolutionary algorithms with spatially structured populations. Based on thefitness-level method, it yields upper bounds on the expected parallel runningtime. This allows to rigorously estimate the speedup gained by parallelization.Tailored results are given for common migration topologies: ring graphs, torusgraphs, hypercubes, and the complete graph. Example applications forpseudo-Boolean optimization show that our method is easy to apply and that itgives powerful results. In our examples the possible speedup increases with thedensity of the topology. Surprisingly, even sparse topologies like ring graphslead to a significant speedup for many functions while not increasing the totalnumber of function evaluations by more than a constant factor. We also identifywhich number of processors yield asymptotically optimal speedups, thus givinghints on how to parametrize parallel evolutionary algorithms.

General Upper Bounds on the Running Time of Parallel Evolutionary Algorithms