Foundations of Computational Intelligence

1. Front Matter

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2. Function Approximation

   1. Front Matter

      Pages 1-1

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   2. Machine Learning and Genetic Regulatory Networks: A Review and a Roadmap

      • Christopher Fogelberg, Vasile Palade

      Pages 3-34

   3. Automatic Approximation of Expensive Functions with Active Learning

      • Dirk Gorissen, Karel Crombecq, Ivo Couckuyt, Tom Dhaene

      Pages 35-62

   4. New Multi-Objective Algorithms for Neural Network Training Applied to Genomic Classification Data

      • Marcelo Costa, Thiago Rodrigues, Euler Horta, Antônio Braga, Carmen Pataro, René Natowicz et al.

      Pages 63-82

   5. An Evolutionary Approximation for the Coefficients of Decision Functions within a Support Vector Machine Learning Strategy

      • Ruxandra Stoean, Mike Preuss, Catalin Stoean, Elia El-Darzi, D. Dumitrescu

      Pages 83-114

3. Connectionist Learning

   1. Front Matter

      Pages 115-115

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   2. Meta-learning and Neurocomputing – A New Perspective for Computational Intelligence

      • Ciro Castiello

      Pages 117-142

   3. Three-Term Fuzzy Back-Propagation

      • M. Hadi Mashinchi, Siti Mariyam H. J. Shamsuddin

      Pages 143-158

   4. Entropy Guided Transformation Learning

      • Cícero Nogueira dos Santos, Ruy Luiz Milidiú

      Pages 159-184

   5. Artificial Development

      • Arturo Chavoya

      Pages 185-215

   6. Robust Training of Artificial Feedforward Neural Networks

      • Moumen T. El-Melegy, Mohammed H. Essai, Amer A. Ali

      Pages 217-242

   7. Workload Assignment in Production Networks by Multi Agent Architecture

      • Paolo Renna, Pierluigi Argoneto

      Pages 243-277

4. Knowledge Representation and Acquisition

   1. Front Matter

      Pages 279-279

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   2. Extensions to Knowledge Acquisition and Effect of Multimodal Representation in Unsupervised Learning

      • Daswin De Silva, Damminda Alahakoon, Shyamali Dharmage

      Pages 281-305

   3. A New Implementation for Neural Networks in Fourier-Space

      • Hazem M. El-Bakry, Mohamed Hamada

      Pages 307-330

5. Learning and Visualization

   1. Front Matter

      Pages 331-331

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   2. Dissimilarity Analysis and Application to Visual Comparisons

      • Sébastien Aupetit, Nicolas Monmarché, Pierre Liardet, Mohamed Slimane

      Pages 333-361

   3. Dynamic Self-Organising Maps: Theory, Methods and Applications

      • Arthur L. Hsu, Isaam Saeed, Saman K. Halgamuge

      Pages 363-379

   4. Hybrid Learning Enhancement of RBF Network with Particle Swarm Optimization

      • Sultan Noman, Siti Mariyam Shamsuddin, Aboul Ella Hassanien

      Pages 381-397

Foundations of Computational Intelligence Volume 1: Learning and Approximation: Theoretical Foundations and Applications Learning methods and approximation algorithms are fundamental tools that deal with computationally hard problems and problems in which the input is gradually disclosed over time. Both kinds of problems have a large number of applications arising from a variety of fields, such as algorithmic game theory, approximation classes, coloring and partitioning, competitive analysis, computational finance, cuts and connectivity, inapproximability results, mechanism design, network design, packing and covering, paradigms for design and analysis of approxi- tion and online algorithms, randomization techniques, real-world applications, scheduling problems and so on. The past years have witnessed a large number of interesting applications using various techniques of Computational Intelligence such as rough sets, connectionist learning; fuzzy logic; evolutionary computing; artificial immune systems; swarm intelligence; reinforcement learning, intelligent multimedia processing etc. . In spite of numerous successful applications of C- putational Intelligence in business and industry, it is sometimes difficult to explain the performance of these techniques and algorithms from a theoretical perspective. Therefore, we encouraged authors to present original ideas dealing with the inc- poration of different mechanisms of Computational Intelligent dealing with Lea- ing and Approximation algorithms and underlying processes. This edited volume comprises 15 chapters, including an overview chapter, which provides an up-to-date and state-of-the art research on the application of Computational Intelligence for learning and approximation.

• Approximation
• Computational Intelligence
• Evolution
• Fuzzy
• algorithms
• calculus
• genome
• intelligence
• knowledge representation
• learning
• machine learning
• neural network
• optimization
• supervised learning
• visualization

• College of Business Administration, Quantitative and Information System Department, Kuwait University, Safat, Kuwait

  Aboul-Ella Hassanien

• Center of Excellence for Quantifiable, Quality of Service, Norwegian University of Science & Technology, Trondheim, Norway

  Ajith Abraham

• Department of Computer and Telecommunications Engineering, University ofWestern Macedonia, Kozani, Greece

  Athanasios V. Vasilakos

• Dept. Electrical and Computer Engineering, University of Alberta, Edmonton,Alberta, Canada

  Witold Pedrycz

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