Handbook of Neuroevolution Through Erlang

  • Gene I. Sher

Table of contents

  1. Front Matter
    Pages i-xx
  2. Foundations

    1. Front Matter
      Pages 41-41
    2. Gene I. Sher
      Pages 43-79
    3. Gene I. Sher
      Pages 81-104
    4. Gene I. Sher
      Pages 105-141
  3. Neuroevolution: Taking the First Step

    1. Front Matter
      Pages 151-151
    2. Gene I. Sher
      Pages 153-185
    3. Gene I. Sher
      Pages 347-395
  4. A Case Study

    1. Front Matter
      Pages 397-397
    2. Gene I. Sher
      Pages 399-444
  5. Advanced Neuroevolution: Creating the Cutting Edge

    1. Front Matter
      Pages 445-445
    2. Gene I. Sher
      Pages 547-571
    3. Gene I. Sher
      Pages 609-659
    4. Gene I. Sher
      Pages 661-734
    5. Gene I. Sher
      Pages 735-752
  6. Applications

    1. Front Matter
      Pages 753-753
    2. Gene I. Sher
      Pages 755-783
    3. Gene I. Sher
      Pages 785-824
  7. Promises Kept

    1. Front Matter
      Pages 825-827
    2. Gene I. Sher
      Pages 829-830
  8. Back Matter
    Pages 831-831

About this book


Handbook of Neuroevolution Through Erlang presents both the theory behind, and the methodology of, developing a neuroevolutionary-based computational intelligence system using Erlang. With a foreword written by Joe Armstrong, this handbook offers an extensive tutorial for creating a state of the art Topology and Weight Evolving Artificial Neural Network (TWEANN) platform. In a step-by-step format, the reader is guided from a single simulated neuron to a complete system. By following these steps, the reader will be able to use novel technology to build a TWEANN system, which can be applied to Artificial Life simulation, and Forex trading. Because of Erlang’s architecture, it perfectly matches that of evolutionary and neurocomptational systems. As a programming language, it is a concurrent, message passing paradigm which allows the developers to make full use of the multi-core & multi-cpu systems. Handbook of Neuroevolution Through Erlang explains how to leverage Erlang’s features in the field of machine learning, and the system’s real world applications, ranging from algorithmic financial trading to artificial life and robotics.


Algorithmic trading Artificial Life Artificial intelligence Computational intelligence Computer vision Erlang Evolutionary computation Genetic algorithm Genetic programming Machine learning Neural network Robotics Soft computing Time series analysis dxnn

Authors and affiliations

  • Gene I. Sher
    • 1
  1. 1.University of Central FloridaOrlandoUSA

Bibliographic information

  • DOI
  • Copyright Information Springer Science+Business Media New York 2013
  • Publisher Name Springer, New York, NY
  • eBook Packages Computer Science
  • Print ISBN 978-1-4614-4462-6
  • Online ISBN 978-1-4614-4463-3
  • Buy this book on publisher's site
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