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DXNN: A Case Study

  • Gene I. Sher
Chapter

Abstract

This chapter presents a case study of a memetic algorithm based TWEANN system that I developed in Erlang, called DXNN. Here we will discuss how DXNN functions, how it is implemented, and the various details and implementation choices I made while building it, and why. We also discuss the various features that it has, the features which we will eventually need to add to the system we’re building together. Our system has a much cleaner and decoupled implementation, and which by the time we’ve reached the last chapter will supersede DXNN in every way.

Keywords

Mutation Operator Sensory Signal Synaptic Weight Tuning Phase Committee Machine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    DXNN’s records.hrl is available at: https://github.com/CorticalComputer/DXNN
  2. 2.
    Sher GI (2010) Discover & eXplore Neural Network (DXNN) Platform, a Modular TWEANN. Available at: http://arxiv.org/abs/1008.2412
  3. 3.
    Gauci J, Stanley KO (2007) Generating Large-Scale Neural Networks Through Discovering Geometric Regularities. Proceedings of the 9th annual conference on Genetic and evolutionary computation GECCO 07, 997.Google Scholar
  4. 4.
    Siebel NT, Sommer G (2007) Evolutionary Reinforcement Learning of Artificial Neural Networks. International Journal of Hybrid Intelligent Systems 4, 171-183.MATHGoogle Scholar
  5. 5.
  6. 6.
    Risi S, Stanley KO (2010) Indirectly Encoding Neural Plasticity as a Pattern of Local Rules. Neural Plasticity 6226, 1-11.Google Scholar
  7. 7.
    Woolley BG, Stanley KO (2010) Evolving a Single Scalable Controller for an Octopus Arm with a Variable Number of Segments. Parallel Problem Solving from Nature PPSN XI, 270-279.Google Scholar
  8. 8.
    DXNN Research Group: www.DXNNResearch.com
  9. 9.
  10. 10.
    DXNN Neural Network Research Repository: www.DXNNResearch.com/NNRR
  11. 11.
    Prdator Vs. Prey Simulation recording: http://www.youtube.com/watch?v=HzsDZt8EO70&feature=related
  12. 12.
    Sher GI (2012) Evolving Chart Pattern Sensitive Neural Network Based Forex TradingAgents. Available at: http://arxiv.org/abs/1111.5892.

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Gene I. Sher
    • 1
  1. 1.Department of Electrical Engineering and Computer ScienceUniversity of Central FloridaOrlandoUSA

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