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

Variable selection problems generally present more than a single solutionand, sometimes, it is worth to find as many solutions as possible. The use ofEvolutionary Algorithms applied to this kind of problem proves to be one of thebest methods to find optimal solutions. Moreover, there are variants designedto find all or almost all local optima, known as Niching Genetic Algorithms(NGA). There are several different NGA methods developed in order to achievethis task. The present work compares the behavior of eight different nichingtechniques, applied to a climatic database of four weather stations distributedin Tucuman, Argentina. The goal is to find different sets of input variablesthat have been used as the input variable by the estimation method. Finalresults were evaluated based on low estimation error and low dispersion error,as well as a high number of different results and low computational time. Asecond experiment was carried out to study the capability of the method toidentify critical variables. The best results were obtained with DeterministicCrowding. In contrast, Steady State Worst Among Most Similar and ProbabilisticCrowding showed good results but longer processing times and less ability todetermine the critical factors.

A comparison of different types of Niching Genetic Algorithms for variable selection in solar radiation estimation