<?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>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Doerr, Carola</style></author><author><style face="normal" font="default" size="100%">Bredeche, Nicolas</style></author><author><style face="normal" font="default" size="100%">Alba, Enrique</style></author><author><style face="normal" font="default" size="100%">Bartz-Beielstein, Thomas</style></author><author><style face="normal" font="default" size="100%">Brockhoff, Dimo</style></author><author><style face="normal" font="default" size="100%">Doerr, Benjamin</style></author><author><style face="normal" font="default" size="100%">Eiben, Gusz</style></author><author><style face="normal" font="default" size="100%">Epitropakis, Michael G.</style></author><author><style face="normal" font="default" size="100%">Fonseca, Carlos M.</style></author><author><style face="normal" font="default" size="100%">Guerreiro, Andreia</style></author><author><style face="normal" font="default" size="100%">Haasdijk, Evert</style></author><author><style face="normal" font="default" size="100%">Heinerman, Jacqueline</style></author><author><style face="normal" font="default" size="100%">Hubert, Julien</style></author><author><style face="normal" font="default" size="100%">Lehre, Per Kristian</style></author><author><style face="normal" font="default" size="100%">Malagò, Luigi</style></author><author><style face="normal" font="default" size="100%">Merelo, J. J.</style></author><author><style face="normal" font="default" size="100%">Miller, Julian</style></author><author><style face="normal" font="default" size="100%">Naujoks, Boris</style></author><author><style face="normal" font="default" size="100%">Oliveto, Pietro</style></author><author><style face="normal" font="default" size="100%">Picek, Stjepan</style></author><author><style face="normal" font="default" size="100%">Pillay, Nelishia</style></author><author><style face="normal" font="default" size="100%">Preuss, Mike</style></author><author><style face="normal" font="default" size="100%">Ryser-Welch, Patricia</style></author><author><style face="normal" font="default" size="100%">Squillero, Giovanni</style></author><author><style face="normal" font="default" size="100%">Stork, Jörg</style></author><author><style face="normal" font="default" size="100%">Sudholt, Dirk</style></author><author><style face="normal" font="default" size="100%">Tonda, Alberto</style></author><author><style face="normal" font="default" size="100%">Whitley, Darrell</style></author><author><style face="normal" font="default" size="100%">Zaefferer, Martin</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Handl, Julia</style></author><author><style face="normal" font="default" size="100%">Hart, Emma</style></author><author><style face="normal" font="default" size="100%">Lewis, Peter R.</style></author><author><style face="normal" font="default" size="100%">López-Ibáñez, Manuel</style></author><author><style face="normal" font="default" size="100%">Ochoa, Gabriela</style></author><author><style face="normal" font="default" size="100%">Paechter, Ben</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Tutorials at PPSN 2016</style></title><secondary-title><style face="normal" font="default" size="100%">Parallel Problem Solving from Nature – PPSN XIV: 14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-319-45823-6_95</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%">1012–1022</style></pages><isbn><style face="normal" font="default" size="100%">978-3-319-45823-6</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">PPSN 2016 hosts a total number of 16 tutorials covering a broad range of current research in evolutionary computation. The tutorials range from introductory to advanced and specialized but can all be attended without prior requirements. All PPSN attendees are cordially invited to take this opportunity to learn about ongoing research activities in our field!</style></abstract></record><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%">Kocsis, Zoltan A.</style></author><author><style face="normal" font="default" size="100%">Neumann, Geoff</style></author><author><style face="normal" font="default" size="100%">Swan, Jerry</style></author><author><style face="normal" font="default" size="100%">Epitropakis, Michael G.</style></author><author><style face="normal" font="default" size="100%">Brownlee, Alexander E. I.</style></author><author><style face="normal" font="default" size="100%">Haraldsson, Sami O.</style></author><author><style face="normal" font="default" size="100%">Bowles, Edward</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Le Goues, Claire</style></author><author><style face="normal" font="default" size="100%">Yoo, Shin</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Repairing and Optimizing Hadoop hashCode Implementations</style></title><secondary-title><style face="normal" font="default" size="100%">Search-Based Software Engineering: 6th International Symposium, SSBSE 2014, Fortaleza, Brazil, August 26-29, 2014. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-319-09940-8_22</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%">259–264</style></pages><isbn><style face="normal" font="default" size="100%">978-3-319-09940-8</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We describe how contract violations in Java TM hashCode methods can be repaired using novel combination of semantics-preserving and generative methods, the latter being achieved via Automatic Improvement Programming. The method described is universally applicable. When applied to the Hadoop platform, it was established that it produces hashCode functions that are at least as good as the original, broken method as well as those produced by a widely-used alternative method from the ‘Apache Commons’ library.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Xiaodong Li</style></author><author><style face="normal" font="default" size="100%">Andries Engelbrecht</style></author><author><style face="normal" font="default" size="100%">M. G. Epitropakis</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Benchmark Functions for CEC'2013 Special Session and Competition on Niching Methods for Multimodal Function Optimization'</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://goanna.cs.rmit.edu.au/~xiaodong/cec13-niching/competition/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Evolutionary Computation and Machine Learning Group, RMIT University</style></publisher><pub-location><style face="normal" font="default" size="100%">Melbourne, Australia</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M. G. Epitropakis</style></author><author><style face="normal" font="default" size="100%">Xiaodong Li</style></author><author><style face="normal" font="default" size="100%">Edmund K. Burke</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Dynamic Archive Niching Differential Evolution Algorithm for Multimodal Optimization</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Congress on Evolutionary Computation, 2013. CEC 2013</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">June</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6557556</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Cancun, Mexico</style></pub-location><pages><style face="normal" font="default" size="100%">79-86</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Highly multimodal landscapes with multiple local/global optima represent common characteristics in real-world applications. Many niching algorithms have been proposed in the literature which aim to search such landscapes in an attempt to locate as many global optima as possible. However, to locate and maintain a large number of global solutions, these algorithms are substantially influenced by their parameter values, such as a large population size. Here, we propose a new niching Differential Evolution algorithm that attempts to overcome the population size influence and produce good performance almost independently of its population size. To this end, we incorporate two mechanisms into the algorithm: a control parameter adaptation technique and an external dynamic archive along with a reinitialization mechanism. The first mechanism is designed to efficiently adapt the control parameters of the algorithm, whilst the second one is responsible for enabling the algorithm to investigate unexplored regions of the search space and simultaneously keep the best solutions found by the algorithm. The proposed approach is compared with two Differential Evolution variants on a recently proposed benchmark suite. Empirical results indicate that the proposed niching algorithm is competitive and very promising. It exhibits a robust and stable behavior, whilst the incorporation of the dynamic archive seems to tackle the population size influence effectively. Moreover, it alleviates the problem of having to fine-tune the population size parameter in a niching algorithm.</style></abstract></record></records></xml>