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.
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References
DXNN’s records.hrl is available at: https://github.com/CorticalComputer/DXNN
Sher GI (2010) Discover & eXplore Neural Network (DXNN) Platform, a Modular TWEANN. Available at: http://arxiv.org/abs/1008.2412
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DXNN Research Group: www.DXNNResearch.com
OpenSPARC: http://www.opensparc.net/
DXNN Neural Network Research Repository: www.DXNNResearch.com/NNRR
Prdator Vs. Prey Simulation recording: http://www.youtube.com/watch?v=HzsDZt8EO70&feature=related
Sher GI (2012) Evolving Chart Pattern Sensitive Neural Network Based Forex TradingAgents. Available at: http://arxiv.org/abs/1111.5892.
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Sher, G.I. (2013). DXNN: A Case Study. In: Handbook of Neuroevolution Through Erlang. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4463-3_10
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DOI: https://doi.org/10.1007/978-1-4614-4463-3_10
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