Computational Intelligence and Bioinspired Systems

1. Front Matter

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2. Mathematical and Theoretical Methods

   1. Role of Function Complexity and Network Size in the Generalization Ability of Feedforward Networks

      • Leonardo Franco, José M. Jerez, José M. Bravo

      Pages 1-8

   2. Analysis of the Sanger Hebbian Neural Network

      • J. Andrés Berzal, Pedro J. Zufiria

      Pages 9-16

   3. Considering Multidimensional Information Through Vector Neural Networks

      • J. L. Crespo, R. J. Duro

      Pages 17-24

   4. Combining Ant Colony Optimization with Dynamic Programming for Solving the k-Cardinality Tree Problem

      • Christian Blum, Maria Blesa

      Pages 25-33

3. Evolutionary Computation

   1. A Basic Approach to Reduce the Complexity of a Self-generated Fuzzy Rule-Table for Function Approximation by Use of Symbolic Interpolation

      • G. Rubio, H. Pomares

      Pages 34-41

   2. Average Time Complexity of Estimation of Distribution Algorithms

      • C. González, A. Ramírez, J. A. Lozano, P. Larrañaga

      Pages 42-49

   3. A Comparison of Evolutionary Approaches to the Shortest Common Supersequence Problem

      • Carlos Cotta

      Pages 50-58

   4. Simultaneous Evolution of Neural Network Topologies and Weights for Classification and Regression

      • Miguel Rocha, Paulo Cortez, José Neves

      Pages 59-66

   5. Applying Bio-inspired Techniques to the p-Median Problem

      • E. Domínguez, J. Muñoz

      Pages 67-74

   6. Optimal Strategy for Resource Allocation of Two-Dimensional Potts Model Using Genetic Algorithm

      • Wing Keung Cheung, Kwok Yip Szeto

      Pages 75-82

   7. Memetic Algorithms to Product-Unit Neural Networks for Regression

      • Francisco Martínez-Estudillo, César Hervás-Martínez, Alfonso Martínez-Estudillo, Domingo Ortíz-Boyer

      Pages 83-90

   8. Lamarckian Clonal Selection Algorithm Based Function Optimization

      • Wuhong He, Haifeng Du, Licheng Jiao, Jing Li

      Pages 91-98

4. Neurocomputational Inspired Models

   1. Artificial Neural Networks Based on Brain Circuits Behaviour and Genetic Algorithms

      • Ana Porto, Alejandro Pazos, Alfonso Araque

      Pages 99-106

   2. Modeling Synaptic Transmission and Quantifying Information Transfer in the Granular Layer of the Cerebellum

      • Egidio D’Angelo, Thierry Nieus, Michele Bezzi, Angelo Arleo, Olivier J. -M. D. Coenen

      Pages 107-114

   3. The After-Hyperpolarization Amplitude and the Rise Time Constant of IPSC Affect the Synchronization Properties of Networks of Inhibitory Interneurons

      • Angelo Di Garbo, Alessandro Panarese, Michele Barbi, Santi Chillemi

      Pages 115-121

   4. TiViPE Simulation of a Cortical Crossing Cell Model

      • Tino Lourens, Emilia Barakova

      Pages 122-129

   5. A Model of Spiking-Bursting Neuronal Behavior Using a Piecewise Linear Two-Dimensional Map

      • Carlos Aguirre, Doris Campos, Pedro Pascual, Eduardo Serrano

      Pages 130-135

   6. Real-Time Spiking Neural Network: An Adaptive Cerebellar Model

      • Christian Boucheny, Richard Carrillo, Eduardo Ros, Olivier J. -M. D. Coenen

      Pages 136-144

   7. Modeling Neural Processes in Lindenmayer Systems

      • Carlos Martín-Vide, Tseren-Onolt Ishdorj

      Pages 145-152

We present in this volume the collection of finally accepted papers of the eighth edition of the “IWANN” conference (“International Work-Conference on Artificial Neural Networks”). This biennial meeting focuses on the foundations, theory, models and applications of systems inspired by nature (neural networks, fuzzy logic and evolutionary systems). Since the first edition of IWANN in Granada (LNCS 540, 1991), the Artificial Neural Network (ANN) community, and the domain itself, have matured and evolved. Under the ANN banner we find a very heterogeneous scenario with a main interest and objective: to better understand nature and beings for the correct elaboration of theories, models and new algorithms. For scientists, engineers and professionals working in the area, this is a very good way to get solid and competitive applications. We are facing a real revolution with the emergence of embedded intelligence in many artificial systems (systems covering diverse fields: industry, domotics, leisure, healthcare, … ). So we are convinced that an enormous amount of work must be, and should be, still done. Many pieces of the puzzle must be built and placed into their proper positions, offering us new and solid theories and models (necessary tools) for the application and praxis of these current paradigms. The above-mentioned concepts were the main reason for the subtitle of the IWANN 2005 edition: “Computational Intelligence and Bioinspired Systems.” The call for papers was launched several months ago, addressing the following topics: 1. Mathematical and theoretical methods in computational intelligence.

• Support Vector Machine
• artificial neural network
• cognition
• computational intelligence
• evolution
• evolutionary computation
• learning
• neural network
• pattern recognition
• robot
• robotics
• algorithm analysis and problem complexity

• Departamento de Ingeniería Electrónica, Universitat Politècnica de Catalunya (UPC). E.T.S.I. de Telecomunicación, Barcelona, Spain

  Joan Cabestany

• Department of Computer Architecture and Computer Technology, University of Granada,  

  Alberto Prieto

• Grupo ISIS, Dpto. Tecnología Electrónica ETSI Telecomunicación, Universidad de Málaga, Málaga, Spain

  Francisco Sandoval

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