<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gexiang Zhang</style></author><author><style face="normal" font="default" size="100%">Miguel A. Gutiérrez-Naranjo</style></author><author><style face="normal" font="default" size="100%">Yanhui Qin</style></author><author><style face="normal" font="default" size="100%">Marian Gheorghe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Membrane-Inspired Evolutionary Algorithm with a Population P System and its Application to Distribution System Reconfiguration</style></title><secondary-title><style face="normal" font="default" size="100%">Tenth Brainstorming Week on Membrane Computing</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Proceedings of the Tenth Brainstorming Week on Membrane Computing</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Membrane computing; membrane-inspired evolutionary algorithm; population P system; distribution system reconﬁguration</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">02/2012</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.gcn.us.es/10BWMC/10BWMCvolII/papers/psma_bwmc.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Fénix Editora</style></publisher><pub-location><style face="normal" font="default" size="100%">Seville, Spain</style></pub-location><volume><style face="normal" font="default" size="100%">II</style></volume><pages><style face="normal" font="default" size="100%">277-292</style></pages><abstract><style face="normal" font="default" size="100%">This paper develops a membrane-inspired evolutionary algorithm, PSMA,
which is designed by using a population P system and a quantum-inspired evolutionary algorithm (QIEA). We use a population P system with three cells to organize three
types of QIEAs, where communications between cells are performed at the level of genes,
instead of the level of individuals reported in the existing membrane algorithms in the
literature. Knapsack problems are applied to discuss the parameter setting and to test
the eﬀectiveness of PSMA. Experimental results show that PSMA is superior to four representative QIEAs and our previous work with respect to the quality of solutions and the
elapsed time. We also use PSMA to solve the optimal distribution system reconﬁguration
problem in power systems for minimizing the power loss.</style></abstract></record></records></xml>