<?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><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Epitropakis, M.G.</style></author><author><style face="normal" font="default" size="100%">Caraffini, F.</style></author><author><style face="normal" font="default" size="100%">Neri, F.</style></author><author><style face="normal" font="default" size="100%">Burke, E.K.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Separability Prototype for Automatic Memes with Adaptive Operator Selection</style></title><secondary-title><style face="normal" font="default" size="100%">Foundations of Computational Intelligence (FOCI), 2014 IEEE Symposium on</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adaptation models</style></keyword><keyword><style  face="normal" font="default" size="100%">adaptive model</style></keyword><keyword><style  face="normal" font="default" size="100%">adaptive operator selection</style></keyword><keyword><style  face="normal" font="default" size="100%">Algorithm design and analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">algorithmics</style></keyword><keyword><style  face="normal" font="default" size="100%">automatic design</style></keyword><keyword><style  face="normal" font="default" size="100%">Benchmark testing</style></keyword><keyword><style  face="normal" font="default" size="100%">hyper-heuristics</style></keyword><keyword><style  face="normal" font="default" size="100%">memetic computing</style></keyword><keyword><style  face="normal" font="default" size="100%">optimisation</style></keyword><keyword><style  face="normal" font="default" size="100%">optimization</style></keyword><keyword><style  face="normal" font="default" size="100%">optimization problems</style></keyword><keyword><style  face="normal" font="default" size="100%">Prototypes</style></keyword><keyword><style  face="normal" font="default" size="100%">search algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">search problems</style></keyword><keyword><style  face="normal" font="default" size="100%">search process</style></keyword><keyword><style  face="normal" font="default" size="100%">separability prototype for automatic memes</style></keyword><keyword><style  face="normal" font="default" size="100%">Software algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">software prototype</style></keyword><keyword><style  face="normal" font="default" size="100%">software prototyping</style></keyword><keyword><style  face="normal" font="default" size="100%">SPAM-AOS</style></keyword><keyword><style  face="normal" font="default" size="100%">Unsolicited electronic mail</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Dec</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">One of the main challenges in algorithmics in general, and in Memetic Computing, in particular, is the automatic design of search algorithms. A recent advance in this direction (in terms of continuous problems) is the development of a software prototype that builds up an algorithm based upon a problem analysis of its separability. This prototype has been called the Separability Prototype for Automatic Memes (SPAM). This article modifies the SPAM by incorporating within it an adaptive model used in hyper-heuristics for tackling optimization problems. This model, namely Adaptive Operator Selection (AOS), rewards at run time the most promising heuristics/memes so that they are more likely to be used in the following stages of the search process. The resulting framework, here referred to as SPAM-AOS, has been tested on various benchmark problems and compared with modern algorithms representing the-state-of-the-art of search for continuous problems. Numerical results show that the proposed SPAM-AOS is a promising framework that outperforms the original SPAM and other modern algorithms. Most importantly, this study shows how certain areas of Memetic Computing and Hyper-heuristics are very closely related topics and it also shows that their combination can lead to the development of powerful algorithmic frameworks.</style></abstract></record></records></xml>