Learning and Intelligent Optimization: 9th International Conference, LION 9, Lille, France, January 12-15, 2015. Revised Selected Papers PDF
Preview Learning and Intelligent Optimization: 9th International Conference, LION 9, Lille, France, January 12-15, 2015. Revised Selected Papers
Clarisse Dhaenens Laetitia Jourdan Marie-Eléonore Marmion (Eds.) 4 9 Learning and 9 8 S C Intelligent Optimization N L 9th International Conference, LION 9 Lille, France, January 12–15, 2015 Revised Selected Papers 123 Lecture Notes in Computer Science 8994 Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen Editorial Board David Hutchison Lancaster University, Lancaster, UK Takeo Kanade Carnegie Mellon University, Pittsburgh, PA, USA Josef Kittler University of Surrey, Guildford, UK Jon M. Kleinberg Cornell University, Ithaca, NY, USA Friedemann Mattern ETH Zurich, Zürich, Switzerland John C. Mitchell Stanford University, Stanford, CA, USA Moni Naor Weizmann Institute of Science, Rehovot, Israel C. Pandu Rangan Indian Institute of Technology, Madras, India Bernhard Steffen TU Dortmund University, Dortmund, Germany Demetri Terzopoulos University of California, Los Angeles, CA, USA Doug Tygar University of California, Berkeley, CA, USA Gerhard Weikum Max Planck Institute for Informatics, Saarbrücken, Germany More information about this series at http://www.springer.com/series/7407 Clarisse Dhaenens Laetitia Jourdan (cid:129) é Marie-El onore Marmion (Eds.) Learning and Intelligent Optimization 9th International Conference, LION 9 – Lille, France, January 12 15, 2015 Revised Selected Papers 123 Editors Clarisse Dhaenens Marie-EléonoreMarmion Lille University Lille University Villeneuve d’Ascq Villeneuve d’Ascq France France Laetitia Jourdan Lille University Villeneuve d’Ascq France ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notesin Computer Science ISBN 978-3-319-19083-9 ISBN978-3-319-19084-6 (eBook) DOI 10.1007/978-3-319-19084-6 LibraryofCongressControlNumber:2015939426 LNCSSublibrary:SL1–TheoreticalComputerScienceandGeneralIssues SpringerChamHeidelbergNewYorkDordrechtLondon ©SpringerInternationalPublishingSwitzerland2015 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartofthe material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodologynow knownorhereafterdeveloped. Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant protectivelawsandregulationsandthereforefreeforgeneraluse. Thepublisher,theauthorsandtheeditorsaresafetoassumethattheadviceandinformationinthisbookare believedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsortheeditors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissionsthatmayhavebeenmade. Printedonacid-freepaper SpringerInternationalPublishingAGSwitzerlandispartofSpringerScience+BusinessMedia (www.springer.com) Preface The large variety of heuristic algorithms for hard optimization problems raises numerous interesting and challenging issues. Practitioners are confronted with the burden of selecting themost appropriate method, in many cases through an expensive algorithm configuration and parameter tuning process, and subject to a steep learning curve. Scientists seek theoretical insights and demand a sound experimental method- ology for evaluating algorithms and assessing strengths and weaknesses. A necessary prerequisite for this effort is a clear separation between the algorithm and the experi- menter, who, in too many cases, is “in the loop” as a crucial intelligent learning component. Both issues are related to designing and engineering ways of “learning” abouttheperformanceofdifferenttechniques,andwaysofusingpastexperienceabout the algorithm behavior to improve performance in the future. This is the scope of the Learning and Intelligent OptimizatioN (LION) conference series. This volume contains papers presented at the 9th LION (Learning and Intelligent OptimizatioN) conference held during January 12–15, 2015 in Lille, France. Thismeeting,whichcontinuesthesuccessfulseriesofLIONevents(seeLION5in Rome–Italy, LION 6 in Paris–France, LION 7 in Catania–Italy, and LION 8 in Gainesville–USA), is exploring the intersections and uncharted territories between machinelearning,artificialintelligence,mathematicalprogramming,andalgorithmsfor hardoptimizationproblems.Themainpurposeoftheeventistobringtogetherexperts from these areas to discuss new ideas and methods, challenges and opportunities in variousapplicationareas,generaltrends,andspecificdevelopments.Optimizationand machinelearningresearchersarenowformingtheirown communityandidentity.The International Conference on Learning and Optimization is proud to be the premiere conference in the area. Atotalof58papersweresubmittedtoLION 9:43submissionsoflongpapersand 15 submissions of short papers. Each manuscript was independently reviewed by at least three members of the Program Committee. 14 long papers and 17 short papers were accepted (some long submissions have been asked to be shortened). Hence, the selection rate for long papers is of 33 %. During the conference, we were pleased to listen to four plenary speakers: – David Corne, Heriot-Watt University, UK. Psychic machines: mind-reading with machine learning – Alex Freitas, University of Kent, UK. Automating the Design of Decision Tree Algorithms with Evolutionary Computation – Daniel Le Berre, Artois University, Lens, France. From Boolean Satisfaction to Boolean Optimization: Application to Dependency Management – Remi Munos, Inria Lille Nord Europe, France. The optimistic principle applied to function optimization and planning VI Preface In addition, two tutorials were presented: – Thomas Stützle, FNRS-IRIDIA, ULB, Belgium. Automatic Algorithm Configu- ration: From Parameter Tuning to Automatic Design – Sébastien Verel, Littoral Côte d’Opale University, Calais, France. Fitness land- scape: the metaphor and beyond January 2015 Clarisse Dhaenens Laetitia Jourdan Marie-Eléonore Marmion Organization Program Committee Hernán Aguirre Shinshu University, Japan Roberto Battiti Università di Trento, Italy Mauro Birattari IRIDIA, Université Libre de Bruxelles, Belgium Christian Blum IKERBASQUE,BasqueFoundationforScience,Spain Juergen Branke University of Warwick, UK Dimo Brockhoff Inria Lille - Nord Europe, France Mauro Brunato University of Trento, Italy Philippe Codognet JFLI - CNRS/UPMC/University of Tokyo, Japan David Cornforth University of Newcastle, Australia Clarisse Dhaenens CRIStAL/Inria/Lille University, France Luca Di Gaspero DIEGM - University of Udine, Italy Karl Doerner University of Vienna, Austria Madalina Drugan Vrije Universiteit Brussel, Belgium Andries Engelbrecht University of Pretoria, South Africa Valerio Freschi University of Urbino, Italy Pablo Garcia University of Granada, Spain Deon Garrett Icelandic Institute for Intelligent Machines, Iceland Michel Gendreau École Polytechnique de Montréal, Canada Adrien Goëffon LERIA - Université d’Angers, France Walter Gutjahr University of Vienna, Austria Youssef Hamadi Microsoft Research, UK Jin-Kao Hao University of Angers, France Geir Hasle SINTEF ICT, Norway Alfredo Hernández-Díaz Pablo de Olavide University (Seville), Spain Francisco Herrera University of Granada, Spain Tomio Hirata School of Information Science, Nagoya University, Japan Holger Hoos University of British Columbia, Canada Frank Hutter University of British Columbia, Canada Helga Ingimundardóttir University of Iceland, Iceland Hisao Ishibuchi Osaka Prefecture University, Japan Yaochu Jin University of Surrey, UK Laetitia Jourdan CRIStAL/Inria/Lille University, France Zeynep Kiziltan University of Bologna, Italy Dario Landa-Silva University of Nottingham, UK Frederic Lardeux LERIA - University of Angers, France Julien Lepagnot LMIA University of Haute Alsace, France VIII Organization Xiaodong Li Wharton School - University of Pennsylvania, USA Arnaud Liefooghe CRIStAL/Inria/Lille University, France Manuel López-Ibáñez IRIDIA/Brussels, Belgium Marie-Eléonore Marmion CRIStAL/Inria/Lille University, France Franco Mascia Université Libre de Bruxelles, Belgium Francesco Masulli University of Genoa, Italy Basseur Matthieu LERIA Angers, France Bernd Meyer Monash University, Australia Marco A. Montes De Oca University of Delaware, USA Irene Moser Swinburne University of Technology, Australia Amir Nakib Université Paris Est Creteil, France Gabriela Ochoa University of Stirling, UK Luis Paquete CISUC, University of Coimbra, Portugal Panos Pardalos University of Florida, USA Andrew J. Parkes University of Nottingham, UK Vincenzo Piuri University of Milan, Italy Mike Preuss TU Dortmund University, Germany Günther Raidl Vienna University of Technology, Austria Steffen Rebennack Colorado School of Mines, USA Celso Ribeiro Universidade Federal Fluminense, Brazil Florian Richoux LINA, University of Nantes, France Eduardo Rodriguez-Tello CINVESTAV-Tamaulipas, Mexico Horst Samulowitz IBM Research, USA Frédéric Saubion LERIA - University of Angers, France Andrea Schaerf University of Udine, Italy Marc Schoenauer Inria Saclay Île-de-France, France Patrick Siarry Université de Paris 12, France Christine Solnon INSA de Lyon, LIRIS, UMR 5205 CNRS, France Thomas Stuetzle IRIDIA, Université libre de Bruxelles, Belgium El-Ghazali Talbi CRIStAL/Inria/Lille University, France Kiyoshi Tanaka Shinshu University, Faculty of Engineering, Japan Ke Tang UniversityofScienceandTechnologyofChina,China Sébastien Verel Université du Littoral Côte d’Opale, France Stefan Voss University of Hamburg, Germany Markus Wagner University of Adelaide, Australia Toby Walsh NICTA/University of New South Wales, Australia David Woodruff University of California, Davis, USA Petros Xanthopoulos University of Central Florida, USA Ning Xiong Mälardalen University, Sweden Saba Yahyaa Vrije Universiteit Brussel, Belgium Saúl Zapotecas-Martinez Cinvestav - IPN Mexico, Mexico Rui Zhang National University of Singapore, Singapore Contents From Sequential Algorithm Selection to Parallel Portfolio Selection . . . . . . . 1 M. Lindauer, Holger H. Hoos, and F. Hutter An Algorithm Selection Benchmark of the Container Pre-marshalling Problem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Kevin Tierney and Yuri Malitsky ADVISER: A Web-Based Algorithm Portfolio Deviser . . . . . . . . . . . . . . . . 23 Mustafa Mısır, Stephanus Daniel Handoko, and Hoong Chuin Lau Identifying Best Hyperparameters for Deep Architectures Using Random Forests. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Zhen-Zhen Li, Zhuo-Yao Zhong, and Lian-Wen Jin Programming by Optimisation Meets Parameterised Algorithmics: A Case Study for Cluster Editing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Sepp Hartung and Holger H. Hoos OSCAR: Online Selection of Algorithm Portfolios with Case Study on Memetic Algorithms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 Mustafa Mısır, Stephanus Daniel Handoko, and Hoong Chuin Lau Learning a Hidden Markov Model-Based Hyper-heuristic. . . . . . . . . . . . . . . 74 Willem Van Onsem, Bart Demoen, and Patrick De Causmaecker Comparison of Parameter Control Mechanisms in Multi-objective Differential Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 Martin Drozdik, Hernan Aguirre, Youhei Akimoto, and Kiyoshi Tanaka Genetic Programming, Logic Design and Case-Based Reasoning for Obstacle Avoidance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 Andy Keane Minimizing Total Tardiness on Identical Parallel Machines Using VNS with Learning Memory. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Eduardo Lalla-Ruiz and Stefan Voß Dynamic Service Selection with Optimal Stopping and ‘Trivial Choice’. . . . . 125 Oliver Skroch A Comparative Study on Self-Adaptive Differential Evolution Algorithms for Test Functions and a Real-World Problem . . . . . . . . . . . . . . . . . . . . . . 131 Shota Eguchi, Yuki Matsugano, Hirokazu Sakaguchi, Satoshi Ono, Hisato Fukuda, Ryo Furukawa, and Hiroshi Kawasaki
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