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Handbook of Neuroevolution Through Erlang

Title
Handbook of Neuroevolution Through Erlang [electronic resource] / by Gene I. Sher.
ISBN
9781461444633
Published
New York, NY : Springer New York : Imprint: Springer, 2013.
Physical Description
XX, 831 p. 172 illus. digital.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
<i>Handbook of Neuroevolution Through Erlang</i> 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. <i>Handbook of Neuroevolution Through Erlang</i> 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.
Variant and related titles
Springer ebooks.
Other formats
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
December 03, 2012
Contents
<p>Introduction: Applications & Motivations
Introduction to Neural Networks
Introduction to Evolutionary Computation
Introduction to Neuroevolutionary Methods
The Unintentional Neural Network Programming Language
Developing a Feed Forward Neural Network
Adding the “Stochastic Hill-Climber” Learning Algorithm
Developing a Simple Neuroevolutionary Platform
Testing the Neuroevolutionary System
DXNN: A Case Study
Decoupling & Modularizing Our Neuroevolutionary Platform
Keeping Track of Important Population and Evolutionary Stats
The Benchmarker
Creating the Two Slightly More Complex Benchmarks
Neural Plasticity
Substrate Encoding
Substrate Plasticity
Artificial Life
Evolving Currency Trading Agents
Conclusion. </p>.
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