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  1. Prototype Based Recognition of Splice Sites

    Splice site recognition is an important subproblem of de novo gene finding, splice junctions constituting the boundary between coding and non-coding...
    Barbara Hammer, Marc Strickert, Thomas Villmann in Bioinformatics Using Computational Intelligence Paradigms
    Chapter
  2. Artificial Neural Networks for Reducing the Dimensionality of Gene Expression Data

    The use of gene chips and microarrays for measuring gene expression is becoming widespread and is producing enormous amounts of data. With increasing...
    Ajit Narayanan, Alan Cheung, ... Christophe Vercellone in Bioinformatics Using Computational Intelligence Paradigms
    Chapter
  3. Computational Complexity of the XCS Classifier System

    Learning classifier systems (LCSs) are online-generalizing rule-based learning systems that use evolutionary computation techniques to evolve an...
    Martin V. Butz, David E. Goldberg, Pier Luca Lanzi in Foundations of Learning Classifier Systems
    Chapter
  4. Conclusions

    This chapter concludes this monograph. It starts with the summary of the progress, results, and status of the research project, followed by tasks of...
    Chapter
  5. A First Improvement: Using Promoters

    Harik [47] took Holland’s call [53] for evolution of tight genetic linkage and proposed the linkage learning genetic algorithm (LLGA), which used a...
    Chapter
  6. Learning in the AMS Context

    In this chapter, we dig further into the notion of “learning” within the AMS context. In conventional connectionist models, the term “learning” is...
    Chapter
  7. Two Simple Learning Classifier Systems

    Since its introduction Holland’s Learning Classifier System (LCS) [Holland, 1976] has inspired much research into ‘genetics-based’ machine learning...
    Chapter
  8. A Mathematical Framework for Studying Learning in Classifier Systems

    Massively parallel, rule-based systems offer both a practical and a theoretical tool for understanding systems that act usefully in complex...
    Chapter
  9. What Makes a Problem Hard for XCS?

    Two basic questions to ask about any learning system are: to what kinds of problems is it well suited? To what kinds of problems is it poorly suited?...
    Tim Kovacs, Manfred Kerber in Foundations of Learning Classifier Systems
    Chapter
  10. Rule Fitness and Pathology in Learning Classifier Systems

    When applied to reinforcement learning, Learning Classifier Systems (LCS) [5] evolve sets of rules in order to maximise the return they receive from...
    Chapter
  11. Convergence Time for the Linkage Learning Genetic Algorithm

    As indicated in the previous chapter, inspired by the coding mechanism existing in genetics, introducing the use of promoters in the linkage learning...
    Chapter
  12. Introducing Subchromosome Representations

    While the linkage learning genetic algorithm achieved successful genetic linkage learning on problems with badly scaled building blocks, it was less...
    Chapter
  13. Basics of Engineering the Hybrid Intelligent Systems – Not Only Industrial Applications

    ComputationalIntelligence(CI) is the methodological framework fitting the highly interdisciplinary requirements featuring most real-world...
    Chapter
  14. No music without melody: How to understand biochemical systems by understanding their dynamics

    The dynamics of the concentration of biochemical species is a systems property that arises through the interaction of metabolites and other...
    Ursula Kummer, Lars Folke Olsen in Systems Biology
    Chapter
  15. Fuzzy Rules Extraction from Connectionist Structures

    In the conjugate effort of building shells for Hybrid Intelligent Systems with a homogenous architecture, based on neural networks, a difficult task...
    Mircea Gh. Negoita, Daniel Neagu, Vasile Palade in Computational Intelligence
    Chapter
  16. Integration of Explicit and Implicit Knowledge in Hybrid Intelligent Systems

    The introduction of modular networks into fuzzy systems provides new insights into the integration of explicit and implicit knowledge in a...
    Mircea Gh. Negoita, Daniel Neagu, Vasile Palade in Computational Intelligence
    Chapter
  17. Practical Implementation Aspects Regarding Real-World Application of Hybrid Intelligent Systems

    This chapter is focussed on the application aspects of HISengineering. The main application areas of HIS are mentioned. A lot of outstanding...
    Mircea Gh. Negoita, Daniel Neagu, Vasile Palade in Computational Intelligence
    Chapter
  18. Intelligent Techniques and Computational Intelligence

    The field of Intelligent Technologies (IT) or Computational Intelligence (CI) is mainly the result of an increasing merger of Fuzzy Systems(FS) or...
    Mircea Gh. Negoita, Daniel Neagu, Vasile Palade in Computational Intelligence
    Chapter
  19. Neuro-Fuzzy Integration in Hybrid Intelligent Systems

    In the last fifteen years, hybrid neural systems have drawn increasing research interest. This approach has been successfully used in various areas,...
    Mircea Gh. Negoita, Daniel Neagu, Vasile Palade in Computational Intelligence
    Chapter
  20. Neuro-Fuzzy Based Hybrid Intelligent Systems for Fault Diagnosis

    In the last ten years, the field of diagnosis has attracted the attention of many researchers, both from the technical area as well as from the...
    Mircea Gh. Negoita, Daniel Neagu, Vasile Palade in Computational Intelligence
    Chapter
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