Nature Inspired Cooperative Strategies for Optimization (NICSO 2010)

1. Front Matter

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2. A Metabolic Subsumption Architecture for Cooperative Control of the e-Puck

   • Verena Fischer, Simon Hickinbotham

   Pages 1-12

3. Social Target Localization in a Population of Foragers

   • Héctor F. Satizábal M., Andres Upegui, Andres Perez-Uribe

   Pages 13-24

4. Using Knowledge Discovery in Cooperative Strategies: Two Case Studies

   • A. D. Masegosa, E. Muñoz, D. Pelta, J. M. Cadenas

   Pages 25-38

5. Hybrid Cooperation Models for the Tool Switching Problem

   • Jhon Edgar Amaya, Carlos Cotta, Antonio J. Fernández Leiva

   Pages 39-52

6. Fault Diagnosis in Industrial Systems Using Bioinspired Cooperative Strategies

   • Lídice Camps Echevarría, Orestes Llanes-Santiago, Antônio José da Silva Neto

   Pages 53-63

7. A New Metaheuristic Bat-Inspired Algorithm

   • Xin-She Yang

   Pages 65-74

8. Evaluation of a Catalytic Search Algorithm

   • Lidia Yamamoto

   Pages 75-87

9. Discovering Beneficial Cooperative Structures for the Automated Construction of Heuristics

   • Germán Terrazas, Dario Landa-Silva, Natalio Krasnogor

   Pages 89-100

10. Eagle Strategy Using Lévy Walk and Firefly Algorithms for Stochastic Optimization

    • Xin-She Yang, Suash Deb

    Pages 101-111

11. CO2RBFN for Short and Medium Term Forecasting of the Extra-Virgin Olive Oil Price

    • M. D. Pérez-Godoy, P. Pérez-Recuerda, María Pilar Frías, A. J. Rivera, C. J. Carmona, Manuel Parras

    Pages 113-125

12. 3D Cell Pattern Generation in Artificial Development

    • Arturo Chavoya, Irma R. Andalon-Garcia, Cuauhtemoc Lopez-Martin, M. E. Meda-Campaña

    Pages 127-139

13. Partial Imitation Rule in Iterated Prisoner Dilemma Game on a Square Lattice

    • Degang Wu, Mathis Antony, K. Y. Szeto

    Pages 141-150

14. A Dynamical Game Model for Sustainable Development

    • D. Dumitrescu, Andrei Sîrghi

    Pages 151-162

15. Studying the Influence of the Objective Balancing Parameter in the Performance of a Multi-Objective Ant Colony Optimization Algorithm

    • A. M. Mora, J. J. Merelo, P. A. Castillo, J. L. J. Laredo, P. García-Sánchez, M. G. Arenas

    Pages 163-176

16. HC12: Highly Scalable Optimisation Algorithm

    • Radomil Matousek

    Pages 177-183

17. Adaptive Evolutionary Testing: An Adaptive Approach to Search-Based Test Case Generation for Object-Oriented Software

    • José Carlos Bregieiro Ribeiro, Mário Alberto Zenha-Rela, Francisco Fernández de Vega

    Pages 185-197

18. Evolutionary Algorithms for Planar MEMS Design Optimisation: A Comparative Study

    • Elhadj Benkhelifa, Michael Farnsworth, Ashutosh Tiwari, Meiling Zhu

    Pages 199-210

19. A Distributed Service Oriented Framework for Metaheuristics Using a Public Standard

    • P. García-Sánchez, J. González, P. A. Castillo, J. J. Merelo, A. M. Mora, J. L. J. Laredo et al.

    Pages 211-222

20. Cellular Genetic Algorithm on Graphic Processing Units

    • Pablo Vidal, Enrique Alba

    Pages 223-232

Many aspects of Nature, Biology or even from Society have become part of the techniques and algorithms used in computer science or they have been used to enhance or hybridize several techniques through the inclusion of advanced evolution, cooperation or biologically based additions. The previous NICSO workshops were held in Granada, Spain, 2006, Acireale, Italy, 2007, and in Tenerife, Spain, 2008. As in the previous editions, NICSO 2010, held in Granada, Spain, was conceived as a forum for the latest ideas and the state of the art research related to nature inspired cooperative strategies. The contributions collected in this book cover topics including nature-inspired techniques like Genetic Algorithms, Evolutionary Algorithms, Ant and Bee Colonies, Swarm Intelligence approaches, Neural Networks, several Cooperation Models, Structures and Strategies, Agents Models, Social Interactions, as well as new algorithms based on the behaviour of fireflies or bats.

• Natur
• Service
• Software
• algorithms
• development
• evolution
• evolutionary algorithm
• genetic algorithms
• intelligence
• metaheuristic
• microelectromechanical system (MEMS)
• model
• optimization
• science
• swarm intelligence

• Dept. of Computer Science and A.I. E.T.S. Ingeniería Informática y de Telecomunicación , University of Granada, Granada, Spain

  Juan R. González, David Alejandro Pelta, Carlos Cruz

• School of Computer Science , University of Nottingham, Nottingham, UK

  Germán Terrazas

• School of Computer Science, University of Nottingham , Nottingham, UK

  Natalio Krasnogor

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