<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vasilios N. Katsikis</style></author><author><style face="normal" font="default" size="100%">Spyridon D. Mourtas</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Patnaik, Srikanta</style></author><author><style face="normal" font="default" size="100%">Tajeddini, Kayhan</style></author><author><style face="normal" font="default" size="100%">Jain, Vipul</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Portfolio Insurance and Intelligent Algorithms</style></title><secondary-title><style face="normal" font="default" size="100%">Computational Management: Applications of Computational Intelligence in Business Management</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1007/978-3-030-72929-5_14</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><pub-location><style face="normal" font="default" size="100%">Cham</style></pub-location><pages><style face="normal" font="default" size="100%">305 - 323</style></pages><isbn><style face="normal" font="default" size="100%">978-3-030-72929-5</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Minimizing portfolio insurance (PI) costs is an investment strategy of great importance. In this chapter, by converting the classical minimum-cost PI (MCPI) problem to a multi-period MCPI (MPMCPI) problem, we define and investigate the MPMCPI under transaction costs (MPMCPITC) problem as a nonlinear programming (NLP) problem. The problem of MCPI gets more genuine in this way. Given the fact that such NLP problems are widely handled by intelligent algorithms, we are introducing a well-tuned approach that can solve the challenging MPMCPITC problem. In our portfolios’ applications, we use real-world data and, along with some of the best memetic meta-heuristic and commercial methods, we provide a solution to the MPMCPITC problem, and we compare their solutions to each other.</style></abstract></record></records></xml>