000 | 03920nam a22005655i 4500 | ||
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001 | 978-3-030-04735-1 | ||
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_a10.1007/978-3-030-04735-1 _2doi |
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_aGenetic Programming Theory and Practice XVI _h[electronic resource] / _cedited by Wolfgang Banzhaf, Lee Spector, Leigh Sheneman. |
250 | _a1st ed. 2019. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2019. |
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300 |
_aXXI, 234 p. 65 illus., 47 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aGenetic and Evolutionary Computation, _x1932-0175 |
|
505 | 0 | _a1 Exploring Genetic Programming Systems with MAP-Elites -- 2 The Evolutionary Buffet Method -- 3 Emergent Policy Discovery for Visual Reinforcement Learning through Tangled Program Graphs: A Tutorial -- 4 Strong Typing, Swarm Enhancement, and Deep Learning Feature Selection in the Pursuit of Symbolic Regression-Classification -- 5 Cluster Analysis of a Symbolic Regression Search Space -- 6 What else is in an evolved name? Exploring evolvable specificity with SignalGP -- Lexicase Selection Beyond Genetic Programming -- 8 Evolving developmental programs that build neural networks for solving multiple problems -- 9 The Elephant in the Room - Towards the Application of Genetic Programming to Automatic Programming -- 10 Untapped Potential of Genetic Programming: Transfer Learning and Outlier Removal -- 11 Program Search for Machine Learning Pipelines Leveraging Symbolic Planning and Reinforcement Learning. | |
520 | _aThese 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: evolving developmental programs for neural networks solving multiple problems, tangled program, transfer learning and outlier detection using GP, program search for machine learning pipelines in reinforcement learning, automatic programming with GP, new variants of GP, like SignalGP, variants of lexicase selection, and symbolic regression and classification techniques. The volume includes several chapters on best practices and lessons learned from hands-on experience. 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. | ||
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aComputational intelligence. | |
650 | 0 | _aAlgorithms. | |
650 | 1 | 4 | _aArtificial Intelligence. |
650 | 2 | 4 | _aComputational Intelligence. |
650 | 2 | 4 | _aAlgorithms. |
700 | 1 |
_aBanzhaf, Wolfgang. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
700 | 1 |
_aSpector, Lee. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
700 | 1 |
_aSheneman, Leigh. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783030047344 |
776 | 0 | 8 |
_iPrinted edition: _z9783030047368 |
830 | 0 |
_aGenetic and Evolutionary Computation, _x1932-0175 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-04735-1 |
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