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The Evolutionary Computation Bestiary (https://github.com/fcampelo/EC-Bestiary) is an initiative to catalogue the (mostly counterproductive) exhuberance of metaphor-based metaheuristics in the peer-reviewed literature. The project was started and is coordinated by Felipe Campelo (UFMG, Brazil) and Claus Aranha (Univ. Tsukuba, Japan), but it has benefited from contributions by several collaborators, who submit entries to the list. As of 2018 June 19, the list of contributors include (in alphabetical order): Adré Steyn - University of Stellenbosch, South Africa Alberto Franzin - Université Libre de Bruxelles, Belgium André Maravilha - UFMG, Brazil Carlos Fonseca - University of Coimbra, Portugal Ciniro Nametala - UFMG, Brazil Eduardo Hauck - UFJF, Brazil Fabio Daolio - University of Stirling, Scotland UK Fernanda Takahashi - UFMG, Brazil Fernando Otero - University of Kent, England UK Fillipe Goulart - UFMG, Brazil Federico Pagnozzi - Université Libre de Bruxelles, Belgium Iago A. de Carvalho - UFMG, Brazil Iztok Fister Jr. - University of Maribor, Slovenia Jakub Grabski - Poznan University of Technology, Poland Kenneth Sörensen - University of Antwerp, Belgium Lars Magnus Hvattum - Molde University College, Norway Marc Sevaux - Université de Bretagne-Sud, France Marco Mollinetti - University of Tsukuba, Japan Marco Pranzo - Università di Siena, Italy Marcus Ritt - UFRGS, Brazil Nadarajen Veerapen - University of Stirling, Scotland UK Robin Purshouse - University of Sheffield, England UK Rubén Ruiz - Universitat Politècnica de València, Spain Ruud Koot - Universiteit Utrecht, The Netherlands Sara Silva - University of Lisbon, Portugal Sergio A. Rojas - Universidad Distrital de Bogotá, Colombia Silvano Martello - University of Bologna, Italy Stefan Voß - Universität Hamburg, Germany Thomas Jacob Riis Stidsen - Danmarks Tekniske Universitet, Denmark Thomas Stützle - Université Libre de Bruxelles, Belgium
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 4 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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| downloads | 4 |

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