Runarsson / Beyer / Burke | Parallel Problem Solving from Nature - PPSN IX | E-Book | sack.de
E-Book

E-Book, Englisch, Band 4193, 1061 Seiten, eBook

Reihe: Lecture Notes in Computer Science

Runarsson / Beyer / Burke Parallel Problem Solving from Nature - PPSN IX

9th International Conference, Reykjavik, Iceland, September 9-13, 2006, Proceedings

E-Book, Englisch, Band 4193, 1061 Seiten, eBook

Reihe: Lecture Notes in Computer Science

ISBN: 978-3-540-38991-0
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark



We are very pleased to present this LNCS volume, the proceedings of the 9th International Conference on Parallel Problem Solving from Nature (PPSN IX). PPSNisoneofthemostrespectedandhighlyregardedconferenceseriesinevo- tionary computation and natural computing / computation. This biennial event was ?rst held in Dortmund in 1990, and then in Brussels (1992), Jerusalem (1994), Berlin (1996), Amsterdam (1998), Paris (2000), Granada (2002), and Birmingham(2004).PPSNcontinuestobetheconferenceofchoicebyresearchers all over the world, who value its high quality. We received 255 paper submissions this year. After an extensive peer review process involving more than 1000 reviews, the programme committee selected the top 106 papers for inclusion in this volume and, of course, for presentation at the conference. This represents an acceptance rate of 42%. The papers included in this volume cover a wide range of topics, from e- lutionary computation to swarm intelligence and from bio-inspired computing to real-world applications. They represent some of the latest and best research in evolutionary and natural computation. Following the PPSN tradition, all - pers at PPSN IX were presented as posters. There were 7 sessions: each session consisting of around 15 papers. For each session, we covered as wide a range of topics as possible so that participants with di?erent interests could ?nd some relevant papers in every session.
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Zielgruppe


Research

Weitere Infos & Material


Theory.- New Algorithms.- Applications.- Multi-objective Optimization.- Evolutionary Learning.- Representations, Operators, and Empirical Evaluation.


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