Handbook of Neuroevolution Through Erlang

Name: Handbook of Neuroevolution Through Erlang
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ISBN: 9781461444626

Gene I. Sher

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English

Springer-Verlag New York Inc.
06 November 2012

Object-oriented programming (OOP); Software Engineering; Artificial intelligence

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.

By: Gene I. Sher
Imprint: Springer-Verlag New York Inc.
Country of Publication: United States
Edition: 2013 ed.
Dimensions: Height: 235mm, Width: 155mm, Spine: 44mm
Weight: 1.436kg
ISBN: 9781461444626
ISBN 10: 1461444624
Pages: 831
Publication Date: 06 November 2012
Audience: Professional and scholarly , Undergraduate
Format: Hardback
Publisher's Status: Active

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.