LEADER 04208nam  22006375  450 
001 9910437567203321
005 20200703081014.0
010   $a1-283-84888-0
010   $a1-4614-4463-2
024 7 $a10.1007/978-1-4614-4463-3
035   $a(CKB)2670000000278618
035   $a(EBL)1030905
035   $a(OCoLC)820724369
035   $a(SSID)ssj0000791437
035   $a(PQKBManifestationID)11463947
035   $a(PQKBTitleCode)TC0000791437
035   $a(PQKBWorkID)10758432
035   $a(PQKB)10797637
035   $a(DE-He213)978-1-4614-4463-3
035   $a(MiAaPQ)EBC1030905
035   $a(PPN)16830032X
035   $a(EXLCZ)992670000000278618
100   $a20121116d2013      u|             0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 10$aHandbook of Neuroevolution Through Erlang$b[electronic resource] /$fby Gene I. Sher
205   $a1st ed. 2013.
210  1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d2013.
215   $a1 online resource (835 p.)
300   $aDescription based upon print version of record.
311   $a1-4614-4462-4 
320   $aIncludes bibliographical references.
327   $aIntroduction: 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.
330   $aHandbook 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.
606   $aSoftware engineering
606   $aArtificial intelligence
606   $aBioinformatics
606   $aSoftware Engineering/Programming and Operating Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/I14002
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
606   $aComputational Biology/Bioinformatics$3https://scigraph.springernature.com/ontologies/product-market-codes/I23050
615  0$aSoftware engineering.
615  0$aArtificial intelligence.
615  0$aBioinformatics.
615 14$aSoftware Engineering/Programming and Operating Systems.
615 24$aArtificial Intelligence.
615 24$aComputational Biology/Bioinformatics.
676   $a005.13
676   $a005.133
700   $aSher$b Gene I$4aut$4http://id.loc.gov/vocabulary/relators/aut$01058036
906   $aBOOK
912   $a9910437567203321
996   $aHandbook of Neuroevolution Through Erlang$92496631
997   $aUNINA