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020 _a9783319775531
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024 7 _a10.1007/978-3-319-77553-1
_2doi
050 4 _aQA76.9.A43
072 7 _aUMB
_2bicssc
072 7 _aCOM051300
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072 7 _aUMB
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082 0 4 _a518.1
_223
245 1 0 _aGenetic Programming
_h[electronic resource] :
_b21st European Conference, EuroGP 2018, Parma, Italy, April 4-6, 2018, Proceedings /
_cedited by Mauro Castelli, Lukas Sekanina, Mengjie Zhang, Stefano Cagnoni, Pablo García-Sánchez.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXII, 323 p. 80 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
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347 _atext file
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490 1 _aTheoretical Computer Science and General Issues,
_x2512-2029 ;
_v10781
505 0 _aUsing GP Is NEAT: Evolving Compositional Pattern Production Functions -- Evolving the Topology of Large Scale Deep Neural Networks -- Evolving Graphs by Graph Programming -- Pruning Techniques for Mixed Ensembles of Genetic Programming Models -- Analyzing Feature Importance for Metabolomics Using Genetic Programming -- Generating Redundant Features with Unsupervised Multi-Tree Genetic Programming -- On the Automatic Design of a Representation for Grammar-Based Genetic Programming -- Multi-Level Grammar Genetic Programming for Scheduling in Heterogeneous Networks -- Scaling Tangled Program Graphs to Visual Reinforcement Learning in ViZDoom -- Towards In Vivo Genetic Programming: Evolving Boolean Networks to Determine Cell States -- A Multiple Expression Alignment Framework for Genetic Programming -- Multi-Objective Evolution of Ultra-Fast General-Purpose Hash Functions -- A Comparative Study on Crossover in Cartesian Genetic Programming -- Evolving Better RNAfold Structure Prediction -- Geometric Crossover in Syntactic Space -- Investigating A Machine Breakdown Genetic Programming Approach for Dynamic Job Shop Scheduling -- Structurally Layered Representation Learning: Towards Deep Learning Through Genetic Programming -- Comparing Rule Evaluation Metrics for the Evolutionary Discovery of Multi-Relational Association Rules in the Semantic Web -- Genetic Programming Hyperheuristic with Cooperative Coevolution for Dynamic Flexible Job Shop Scheduling. .
520 _aThis book constitutes the refereed proceedings of the 21st European Conference on Genetic Programming, EuroGP 2018, held in Parma, Italy, in April 2018, co-located with the Evo* 2018 events, EvoCOP, EvoMUSART, and EvoApplications. The 11 revised full papers presented together with 8 poster papers were carefully reviewed and selected from 36 submissions. The wide range of topics in this volume reflects the current state of research in the field. Thus, we see topics and applications including analysis of feature importance for metabolomics, semantic methods, evolution of boolean networks, generation of redundant features, ensembles of GP models, automatic design of grammatical representations, GP and neuroevolution, visual reinforcement learning, evolution of deep neural networks, evolution of graphs, and scheduling in heterogeneous networks.
650 0 _aAlgorithms.
_93390
650 0 _aComputer arithmetic and logic units.
_936750
650 0 _aArtificial intelligence.
_93407
650 0 _aData mining.
_93907
650 0 _aData structures (Computer science).
_98188
650 0 _aInformation theory.
_914256
650 1 4 _aAlgorithms.
_93390
650 2 4 _aArithmetic and Logic Structures.
_936752
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aData Mining and Knowledge Discovery.
_9173620
650 2 4 _aData Structures and Information Theory.
_931923
700 1 _aCastelli, Mauro.
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700 1 _aSekanina, Lukas.
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700 1 _aZhang, Mengjie.
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700 1 _aCagnoni, Stefano.
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700 1 _aGarcía-Sánchez, Pablo.
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710 2 _aSpringerLink (Online service)
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776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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830 0 _aTheoretical Computer Science and General Issues,
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