<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">X. Li</style></author><author><style face="normal" font="default" size="100%">M. G. Epitropakis</style></author><author><style face="normal" font="default" size="100%">K. Deb</style></author><author><style face="normal" font="default" size="100%">A. Engelbrecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Seeking Multiple Solutions: an Updated Survey on Niching Methods and Their Applications</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Evolutionary Computation</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Benchmark testing</style></keyword><keyword><style  face="normal" font="default" size="100%">evolutionary computation</style></keyword><keyword><style  face="normal" font="default" size="100%">Meta-heuristics</style></keyword><keyword><style  face="normal" font="default" size="100%">Multi-modal optimization</style></keyword><keyword><style  face="normal" font="default" size="100%">Multi-solution methods</style></keyword><keyword><style  face="normal" font="default" size="100%">Niching methods</style></keyword><keyword><style  face="normal" font="default" size="100%">Optimization methods</style></keyword><keyword><style  face="normal" font="default" size="100%">Problem-solving</style></keyword><keyword><style  face="normal" font="default" size="100%">Sociology</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistics</style></keyword><keyword><style  face="normal" font="default" size="100%">Swarm intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">Two dimensional displays</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><volume><style face="normal" font="default" size="100%">PP</style></volume><pages><style face="normal" font="default" size="100%">1-1</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Multi-Modal Optimization (MMO) aiming to locate multiple optimal (or near-optimal) solutions in a single simulation run has practical relevance to problem solving across many fields. Population-based meta-heuristics have been shown particularly effective in solving MMO problems, if equipped with specificallydesigned diversity-preserving mechanisms, commonly known as niching methods. This paper provides an updated survey on niching methods. The paper first revisits the fundamental concepts about niching and its most representative schemes, then reviews the most recent development of niching methods, including novel and hybrid methods, performance measures, and benchmarks for their assessment. Furthermore, the paper surveys previous attempts at leveraging the capabilities of niching to facilitate various optimization tasks (e.g., multi-objective and dynamic optimization) and machine learning tasks (e.g., clustering, feature selection, and learning ensembles). A list of successful applications of niching methods to real-world problems is presented to demonstrate the capabilities of niching methods in providing solutions that are difficult for other optimization methods to offer. The significant practical value of niching methods is clearly exemplified through these applications. Finally, the paper poses challenges and research questions on niching that are yet to be appropriately addressed. Providing answers to these questions is crucial before we can bring more fruitful benefits of niching to real-world problem solving.</style></abstract></record></records></xml>