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

An experimental comparison of two or more optimization algorithms requiresthe same computational resources to be assigned to each algorithm. When amaximum runtime is set as the stopping criterion, all algorithms need to beexecuted in the same machine if they are to use the same resources.Unfortunately, the implementation code of the algorithms is not alwaysavailable, which means that running the algorithms to be compared in the samemachine is not always possible. And even if they are available, someoptimization algorithms might be costly to run, such as training largeneural-networks in the cloud.  In this paper, we consider the following problem: how do we compare theperformance of a new optimization algorithm B with a known algorithm A in theliterature if we only have the results (the objective values) and the runtimein each instance of algorithm A? Particularly, we present a methodology thatenables a statistical analysis of the performance of algorithms executed indifferent machines. The proposed methodology has two parts. First, we propose amodel that, given the runtime of an algorithm in a machine, estimates theruntime of the same algorithm in another machine. This model can be adjusted sothat the probability of estimating a runtime longer than what it should be isarbitrarily low. Second, we introduce an adaptation of the one-sided sign testthat uses a modified p-value and takes into account that probability. Suchadaptation avoids increasing the probability of type I error associated withexecuting algorithms A and B in different machines.

On the Fair Comparison of Optimization Algorithms in Different Machines