Swarm Intelligence and Data Mining
Foreword (PDF) by James Kennedy, USA
Preface (PDF)
Table of Contents (PDF)
Volume 2: Stigmergic Optimization
Preface (PDF)
Table of Contents (PDF)
Please note that the page numbers listed in the table of contents may vary slightly due to some final adjustment by the publisher.
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Dear Colleagues,
First of all many thanks
for your contributions and making this edited volume a real success! We have
received 35 chapters for the proposed edited volume
focused on 'Swarm Intelligence and Data Mining'. Since more than 50% of the
chapters were related to optimization related themes, we have decided to
divide the contributions into the following two volumes:
-- Swarm
Intelligence and Data Mining
-- Stigmergic Optimization
All authors have been notified. If you are yet to receive our notification email, please let us know immediately. A list of all accepted chapters are displayed below. Authors of accepted chapters are expected to submit the camera ready manuscripts in accordance with the reviewer comments by
January 14, 2006.Thank you for your cooperation
Ajith Abraham, Crina Grosan and Vitorino Ramos
Editors of the Book Volumes
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List of Accepted Book Chapters
Volume 1: Swarm Intelligence and Data Mining
Foreword by Dr. James Kennedy
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Introduction to Swarm Intelligence and Data Mining
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AntMiner+: A max-min ant system building rule-based classifier
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Performing feature selection with ACO
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Simultaneous ant colony optimization algorithms for learning linguistic fuzzy rules
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Ant colony clustering and feature extraction for anomaly intrusion detection
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Data and text mining with hierarchical clustering ants
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Clustering ensemble using ANT and ART
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Swarm clustering based on flowers pollination by artificial bees
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Computer study of the evolution of 'news foragers' on the Internet
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Data swarm clustering
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Particle Swarm Optimization for Pattern Recognition and Image Processing
Volume 2: Stigmergic Optimization
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Stigmergic Optimization: Foundations, perspectives and applications
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Stigmergic autonomous navigation in collective robotics
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A general approach to swarm coordination using circle formation
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Cooperative particle swarm optimizers: a powerful and promising approach
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Parallel particle swarm optimization algorithms with adaptive simulated annealing
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Termite: a swarm intelligent routing algorithm for mobile wireless ad-hoc networks
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Linear multiobjective particle swarm optimization
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Physically realistic self-assembly simulation system
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Gliders and rders: A particle swarm selects for coherent space-time structures in evolving cellular automata
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Stigmergic navigation for multi-agent teams in complex environments
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Intelligence: Theoretical proof that empirical techniques are optimal
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Stochastic diffusion search: Partial function evaluation in swarm intelligence dynamic optimization
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Call for Springer SCI Series Book Chapters
Swarm Intelligence and Data Mining
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SCOPE AND CALL FOR BOOK CHAPTERS:
Swarm Intelligence (SI) indicates a recent computational and behavioral metaphor for solving distributed problems that originally took its inspiration from the biological examples provided by social insects (ants, termites, bees, wasps) and by swarming, flocking, herding behaviors in vertebrates. We seek to explore the applicability of these bio-inspired approaches to the development of self-organizing, evolving, adaptive and autonomous information technologies, which will meet the requirements of next-generation information systems, such as diversity, scalability, robustness, and resilience. This edited volume is targeted to present the latest state-of-the-art methodologies in data mining using SI techniques. Both theoretical papers (preferably including simulations) and application papers related to different data mining methodologies are welcome. |
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TOPIC
INCLUDES BUT IS NOT LIMITED TO: - New SI techniques for clustering, data analyzing, Classification, Sorting, Data Retrieval. - Particle Swarm / Cultural Algorithms. - Complex Adaptive Systems. - Artificial Life as well as other Animal Societies bio-inspired algorithms. - Flocks, Herds and Schools. - Swarms and Cooperative Robotics. - Distributed algorithms, self-regulation, self-repair and self-maintenance ontologies. - Biomedical, multimedia and e-commerce applications. - Hybridization with other methods (e.g. Evolutionary Computation and Neural Networks). |
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BOOK
CHAPTER SUBMISSION:
The book will
published in the Springer Verlag SCI series 'Studies in
Computational Intelligence'.
Please prepare the manuscript using
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