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Computer Science - Software Engineering | Handbook of Neuroevolution Through Erlang

Handbook of Neuroevolution Through Erlang

Sher, Gene I.

2013, XX, 831 p. 172 illus.

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  • Provides a friendly step-by-step guide on the construction of Topology and Weight Evolving Artificial Neural Network systems from start to finish
  • Covers novel material for using Erlang in the construction of TWEANN systems
  • Explains why Neural Network based Computational Intelligence systems map perfectly to Erlang’s architecture, and the importance of this programming language to the future of computational intelligence
  • Introduces new TWEANN algorithms, with the final result being a concurrent, cutting edge, direct and indirect encoded, plasticity enabled, TWEANN platform
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.

Content Level » Professional/practitioner

Keywords » 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

Related subjects » Artificial Intelligence - Bioinformatics - Software Engineering

Table of contents 

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.

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