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03857nam a22005775i 4500 |
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978-3-319-34223-8 |
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DE-He213 |
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20161221110527.0 |
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cr nn 008mamaa |
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161221s2016 gw | s |||| 0|eng d |
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|a 9783319342238
|9 978-3-319-34223-8
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|a 10.1007/978-3-319-34223-8
|2 doi
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|a Q334-342
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|a TJ210.2-211.495
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|a Genetic Programming Theory and Practice XIII
|h [electronic resource] /
|c edited by Rick Riolo, W.P. Worzel, Mark Kotanchek, Arthur Kordon.
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|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2016.
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|a XX, 262 p. 69 illus., 31 illus. in color.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
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|a text file
|b PDF
|2 rda
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|a Genetic and Evolutionary Computation,
|x 1932-0167
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| 505 |
0 |
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|a Evolving Simple Symbolic Regression Models by Multi-objective Genetic Programming -- Learning Heuristics for Mining RNA Sequence-Structure Motifs -- Kaizen Programming for Feature Construction for Classification -- GP as if You Meant It: An Exercise for Mindful Practice -- nPool: Massively Distributed Simultaneous Evolution and Cross-Validation in EC-Star -- Highly Accurate Symbolic Regression with Noisy Training Data -- Using Genetic Programming for Data Science: Lessons Learned -- The Evolution of Everything (EvE) and Genetic Programming -- Lexicase selection for program synthesis: a Diversity Analysis -- Using Graph Databases to Explore the Dynamics of Genetic Programming Runs -- Predicting Product Choice with Symbolic Regression and Classification -- Multiclass Classification Through Multidimensional Clustering -- Prime-Time: Symbolic Regression takes its place in the Real World.
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| 520 |
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|a These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Topics in this volume include: multi-objective genetic programming, learning heuristics, Kaizen programming, Evolution of Everything (EvE), lexicase selection, behavioral program synthesis, symbolic regression with noisy training data, graph databases, and multidimensional clustering. It also covers several chapters on best practices and lesson learned from hands-on experience. Additional application areas include financial operations, genetic analysis, and predicting product choice. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results.
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| 650 |
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|a Computer science.
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| 650 |
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|a Algorithms.
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| 650 |
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|a Artificial intelligence.
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| 650 |
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|a Operations research.
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| 650 |
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|a Management science.
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| 650 |
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|a Computational intelligence.
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|a Computer Science.
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| 650 |
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|a Artificial Intelligence (incl. Robotics).
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| 650 |
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4 |
|a Computational Intelligence.
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| 650 |
2 |
4 |
|a Algorithm Analysis and Problem Complexity.
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| 650 |
2 |
4 |
|a Operations Research, Management Science.
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| 700 |
1 |
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|a Riolo, Rick.
|e editor.
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| 700 |
1 |
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|a Worzel, W.P.
|e editor.
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| 700 |
1 |
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|a Kotanchek, Mark.
|e editor.
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| 700 |
1 |
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|a Kordon, Arthur.
|e editor.
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| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
0 |
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|t Springer eBooks
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| 776 |
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8 |
|i Printed edition:
|z 9783319342214
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| 830 |
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|a Genetic and Evolutionary Computation,
|x 1932-0167
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| 856 |
4 |
0 |
|u http://dx.doi.org/10.1007/978-3-319-34223-8
|z Full Text via HEAL-Link
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| 912 |
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|a ZDB-2-SCS
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| 950 |
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|a Computer Science (Springer-11645)
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