Optimization of Menu Layouts by Means of Genetic Algorithms
Menu systems are key components in modern graphical user interfaces (GUIs), either for traditional desktop applications, or for the latest web applications. The design of interface layout must consider different aspects resulting in a trade-off between of
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Editorial Board David Hutchison Lancaster University, UK Takeo Kanade Carnegie Mellon University, Pittsburgh, PA, USA Josef Kittler University of Surrey, Guildford, UK Jon M. Kleinberg Cornell University, Ithaca, NY, USA Alfred Kobsa University of California, Irvine, CA, USA Friedemann Mattern ETH Zurich, Switzerland John C. Mitchell Stanford University, CA, USA Moni Naor Weizmann Institute of Science, Rehovot, Israel Oscar Nierstrasz University of Bern, Switzerland C. Pandu Rangan Indian Institute of Technology, Madras, India Bernhard Steffen University of Dortmund, Germany Madhu Sudan Massachusetts Institute of Technology, MA, USA Demetri Terzopoulos University of California, Los Angeles, CA, USA Doug Tygar University of California, Berkeley, CA, USA Gerhard Weikum Max-Planck Institute of Computer Science, Saarbruecken, Germany
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Jano van Hemert Carlos Cotta (Eds.)
Evolutionary Computation in Combinatorial Optimization 8th European Conference, EvoCOP 2008 Naples, Italy, March 26-28, 2008 Proceedings
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Volume Editors Jano van Hemert University of Edinburgh, National e-Science Centre 15 South College Street, Edinburgh EH8 9AA, UK E-mail: [email protected] Carlos Cotta Universidad de Málaga, Dept. Lenguajes y Ciencias de la Computación ETSI Informática, Campus de Teatinos, 29071 Málaga, Spain E-mail: [email protected]
Cover illustration: "Ammonite II" by Dennis H. Miller (2004-2005) www.dennismiller.neu.edu
Library of Congress Control Number: 2008922955 CR Subject Classification (1998): F.1, F.2, G.1.6, G.2.1, G.1 LNCS Sublibrary: SL 1 – Theoretical Computer Science and General Issues ISSN ISBN-10 ISBN-13
0302-9743 3-540-78603-1 Springer Berlin Heidelberg New York 978-3-540-78603-0 Springer Berlin Heidelberg New York
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Preface
Metaheuristics have been shown to be effective for difficult combinatorial optimization problems appearing in various industrial, economical, and scientific domains. Prominent examples of metaheuristics are evolutionary algorithms, tabu search, simulated annealing, scatter search, memetic algorithms, variable neighborhood search, iterated local search, greedy randomized adaptive search procedures, ant colony optimization
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