<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">C. P. Brás</style></author><author><style face="normal" font="default" size="100%">A. L. Custódio</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On the use of polynomial models in multiobjective directional direct search</style></title><secondary-title><style face="normal" font="default" size="100%">Computational Optimization and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://link.springer.com/article/10.1007/s10589-020-00233-8</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">77</style></volume><pages><style face="normal" font="default" size="100%">897-918</style></pages><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Polynomial interpolation or regression models are an important tool in Derivativefree Optimization, acting as surrogates of the real function. In this work, we propose the use of these models in the multiobjective framework of directional direct search, namely the one of Direct Multisearch. Previously evaluated points are used to build quadratic polynomial models, which are minimized in an attempt of generating nondominated points of the true function, defining a search step for the algorithm. Numerical results state the competitiveness of the proposed approach.&lt;/p&gt;
</style></abstract></record></records></xml>