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Nature Inspired Cooperative Strategies for Optimization (NICSO 2013)

Learning, Optimization and Interdisciplinary Applications

  • Book
  • © 2014

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Editors:
  1. German Terrazas

    1. School of Computer Science, University of Nottingham, Nottingham, United Kingdom

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  2. Fernando E. B. Otero

    1. School of Computing, University of Kent, Canterbury, United Kingdom

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  3. Antonio D. Masegosa

    1. Center for Research on ICT, University of Granada, Granada, Spain

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  • Recent research on Nature Inspired Cooperative Strategies for Optimizationcrest123
  • Edited outcome of the sixth International Workshop NICSO 2013 Nature Inspired Cooperative Strategies for Optimization held September 2nd - 4th, 2013 at Canterbury, UK
  • Written by leading experts in the field

Part of the book series: Studies in Computational Intelligence (SCI, volume 512)

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About this book

Biological and other natural processes have always been a source of inspiration for computer science and information technology. Many emerging problem solving techniques integrate advanced evolution and cooperation strategies, encompassing a range of spatio-temporal scales for visionary conceptualization of evolutionary computation.

This book is a collection of research works presented in the VI International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO) held in Canterbury, UK. Previous editions of NICSO were held in Granada, Spain (2006 & 2010), Acireale, Italy (2007), Tenerife, Spain (2008), and Cluj-Napoca, Romania (2011). NICSO 2013 and this book provides a place where state-of-the-art research, latest ideas and emerging areas of nature inspired cooperative strategies for problem solving are vigorously discussed and exchanged among the scientific community. The breadth and variety of articles in this book report on nature inspired methods and applications such as Swarm Intelligence, Hyper-heuristics, Evolutionary Algorithms, Cellular Automata, Artificial Bee Colony, Dynamic Optimization, Support Vector Machines, Multi-Agent Systems, Ant Clustering, Evolutionary Design Optimisation, Game Theory and other several Cooperation Models.

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Table of contents (26 chapters)

  1. Front Matter

    Pages 1-12
    Download chapter PDF

  2. Extending the ABC-Miner Bayesian Classification Algorithm

    • Khalid M. Salama, Alex A. Freitas
    Pages 1-12

  3. A Multiple Pheromone Ant Clustering Algorithm

    • Jan Chircop, Christopher D. Buckingham
    Pages 13-27

  4. An Island Memetic Differential Evolution Algorithm for the Feature Selection Problem

    • Magdalene Marinaki, Yannis Marinakis
    Pages 29-42

  5. Using a Scouting Predator-Prey Optimizer to Train Support Vector Machines with non PSD Kernels

    • Arlindo Silva, Teresa Gonçalves
    Pages 43-56

  6. Response Surfaces with Discounted Information for Global Optima Tracking in Dynamic Environments

    • Sergio Morales-Enciso, Juergen Branke
    Pages 57-69

  7. Fitness Based Self Adaptive Differential Evolution

    • Harish Sharma, Pragati Shrivastava, Jagdish Chand Bansal, Ritu Tiwari
    Pages 71-84

  8. Adaptation Schemes and Dynamic Optimization Problems: A Basic Study on the Adaptive Hill Climbing Memetic Algorithm

    • Jenny Fajardo Calderín, Antonio D. Masegosa, Alejandro Rosete Suárez, David A. Pelta
    Pages 85-97

  9. Using Base Position Errors in an Entropy-Based Evaluation Function for the Study of Genetic Code Adaptability

    • Lariza Laura de Oliveira, Renato Tinós
    Pages 99-111

  10. An Adaptive Multi-Crossover Population Algorithm for Solving Routing Problems

    • E. Osaba, E. Onieva, R. Carballedo, F. Diaz, A. Perallos
    Pages 113-124

  11. Corner Based Many-Objective Optimization

    • Hélio Freire, P. B. de Moura Oliveira, E. J. Solteiro Pires, Maximino Bessa
    Pages 125-139

  12. Escaping Local Optima via Parallelization and Migration

    • Vincenzo Cutello, Angelo G. De Michele, Mario Pavone
    Pages 141-152

  13. An Improved Genetic Based Keyword Extraction Technique

    • J. Dafni Rose, Divya D. Dev, C. R. Rene Robin
    Pages 153-166

  14. Part-of-Speech Tagging Using Evolutionary Computation

    • Ana Paula Silva, Arlindo Silva, Irene Rodrigues
    Pages 167-178

  15. A Cooperative Approach Using Ants and Bees for the Graph Coloring Problem

    • Malika Bessedik, Asma Daoudi, Karima Benatchba
    Pages 179-190

  16. Artificial Bee Colony Training of Neural Networks

    • John A. Bullinaria, Khulood AlYahya
    Pages 191-201

  17. Nonlinear Optimization in Landscapes with Planar Regions

    • Eddy Mesa, Juan David Velásquez, Gloria Patricia Jaramillo
    Pages 203-215

  18. Optimizing Neighbourhood Distances for a Variant of Fully-Informed Particle Swarm Algorithm

    • Vytautas Jančauskas
    Pages 217-229

  19. Meta Morphic Particle Swarm Optimization

    • Jesse van den Kieboom, Soha Pouya, Auke Jan Ijspeert
    Pages 231-244

  20. Empirical Study of Computational Intelligence Strategies for Biochemical Systems Modelling

    • Zujian Wu, Crina Grosan, David Gilbert
    Pages 245-260
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Editors and Affiliations

  • School of Computer Science, University of Nottingham, Nottingham, United Kingdom

    German Terrazas

  • School of Computing, University of Kent, Canterbury, United Kingdom

    Fernando E. B. Otero

  • Center for Research on ICT, University of Granada, Granada, Spain

    Antonio D. Masegosa

Bibliographic Information

  • Book Title: Nature Inspired Cooperative Strategies for Optimization (NICSO 2013)

  • Book Subtitle: Learning, Optimization and Interdisciplinary Applications

  • Editors: German Terrazas, Fernando E. B. Otero, Antonio D. Masegosa

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-319-01692-4

  • Publisher: Springer Cham

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer International Publishing Switzerland 2014

  • Hardcover ISBN: 978-3-319-01691-7Published: 10 September 2013

  • Softcover ISBN: 978-3-319-03347-1Published: 14 August 2015

  • eBook ISBN: 978-3-319-01692-4Published: 15 August 2013

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XIII, 355

  • Topics: Computational Intelligence, Artificial Intelligence

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