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.
Table of Content
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.