Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/2676
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dc.contributor.authorCastelli, Mauro. ; editor. ;en_US
dc.contributor.authorSekanina, Lukas. ; editor. ;en_US
dc.contributor.authorZhang, Mengjie. ; editor. ;en_US
dc.contributor.authorCagnoni, Stefano. ; editor. ;en_US
dc.contributor.authorGarcía-Sánchez, Pablo. ; editor. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:39:47Z-
dc.date.available2020-05-17T08:39:47Z-
dc.date.issued2018en_US
dc.identifier.urihttp://localhost/handle/Hannan/2676-
dc.description005.1 ; 23 ;en_US
dc.descriptionen_US
dc.descriptionPrinted edition: ; 9783319775524 ;en_US
dc.descriptionen_US
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractThis 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. ;en_US
dc.description.statementofresponsibilityedited by Mauro Castelli, Lukas Sekanina, Mengjie Zhang, Stefano Cagnoni, Pablo García-Sánchez.en_US
dc.description.tableofcontentsUsing 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. . ;en_US
dc.format.extentXII, 323 p. 80 illus. ; online resource. ;en_US
dc.publisherSpringer International Publishing :en_US
dc.publisherImprint: Springer,en_US
dc.relation.ispartofseriesLecture Notes in Computer Science, ; 0302-9743 ; ; 10781 ;en_US
dc.relation.ispartofseriesLecture Notes in Computer Science, ; 0302-9743 ; ; 10781 ;en_US
dc.relation.haspart9783319775531.pdfen_US
dc.subjectComputer Scienceen_US
dc.subjectArithmetic and logic units, Computer. ;en_US
dc.subjectData structures (Computer science). ;en_US
dc.subjectAlgorithmsen_US
dc.subjectData Miningen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectComputer Scienceen_US
dc.subjectAlgorithm Analysis and Problem Complexity. ;en_US
dc.subjectArithmetic and Logic Structuen_US
dc.titleGenetic Programmingen_US
dc.title.alternative21st European Conference, EuroGP 2018, Parma, Italy, April 4-6, 2018, Proceedings /en_US
dc.typeBooken_US
dc.publisher.placeCham :en_US
dc.classification.lcQA76.9.A43 ;en_US
Appears in Collections:مهندسی فناوری اطلاعات

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Full metadata record
DC FieldValueLanguage
dc.contributor.authorCastelli, Mauro. ; editor. ;en_US
dc.contributor.authorSekanina, Lukas. ; editor. ;en_US
dc.contributor.authorZhang, Mengjie. ; editor. ;en_US
dc.contributor.authorCagnoni, Stefano. ; editor. ;en_US
dc.contributor.authorGarcía-Sánchez, Pablo. ; editor. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:39:47Z-
dc.date.available2020-05-17T08:39:47Z-
dc.date.issued2018en_US
dc.identifier.urihttp://localhost/handle/Hannan/2676-
dc.description005.1 ; 23 ;en_US
dc.descriptionen_US
dc.descriptionPrinted edition: ; 9783319775524 ;en_US
dc.descriptionen_US
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractThis 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. ;en_US
dc.description.statementofresponsibilityedited by Mauro Castelli, Lukas Sekanina, Mengjie Zhang, Stefano Cagnoni, Pablo García-Sánchez.en_US
dc.description.tableofcontentsUsing 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. . ;en_US
dc.format.extentXII, 323 p. 80 illus. ; online resource. ;en_US
dc.publisherSpringer International Publishing :en_US
dc.publisherImprint: Springer,en_US
dc.relation.ispartofseriesLecture Notes in Computer Science, ; 0302-9743 ; ; 10781 ;en_US
dc.relation.ispartofseriesLecture Notes in Computer Science, ; 0302-9743 ; ; 10781 ;en_US
dc.relation.haspart9783319775531.pdfen_US
dc.subjectComputer Scienceen_US
dc.subjectArithmetic and logic units, Computer. ;en_US
dc.subjectData structures (Computer science). ;en_US
dc.subjectAlgorithmsen_US
dc.subjectData Miningen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectComputer Scienceen_US
dc.subjectAlgorithm Analysis and Problem Complexity. ;en_US
dc.subjectArithmetic and Logic Structuen_US
dc.titleGenetic Programmingen_US
dc.title.alternative21st European Conference, EuroGP 2018, Parma, Italy, April 4-6, 2018, Proceedings /en_US
dc.typeBooken_US
dc.publisher.placeCham :en_US
dc.classification.lcQA76.9.A43 ;en_US
Appears in Collections:مهندسی فناوری اطلاعات

Files in This Item:
File Description SizeFormat 
9783319775531.pdf15.07 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorCastelli, Mauro. ; editor. ;en_US
dc.contributor.authorSekanina, Lukas. ; editor. ;en_US
dc.contributor.authorZhang, Mengjie. ; editor. ;en_US
dc.contributor.authorCagnoni, Stefano. ; editor. ;en_US
dc.contributor.authorGarcía-Sánchez, Pablo. ; editor. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:39:47Z-
dc.date.available2020-05-17T08:39:47Z-
dc.date.issued2018en_US
dc.identifier.urihttp://localhost/handle/Hannan/2676-
dc.description005.1 ; 23 ;en_US
dc.descriptionen_US
dc.descriptionPrinted edition: ; 9783319775524 ;en_US
dc.descriptionen_US
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractThis 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. ;en_US
dc.description.statementofresponsibilityedited by Mauro Castelli, Lukas Sekanina, Mengjie Zhang, Stefano Cagnoni, Pablo García-Sánchez.en_US
dc.description.tableofcontentsUsing 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. . ;en_US
dc.format.extentXII, 323 p. 80 illus. ; online resource. ;en_US
dc.publisherSpringer International Publishing :en_US
dc.publisherImprint: Springer,en_US
dc.relation.ispartofseriesLecture Notes in Computer Science, ; 0302-9743 ; ; 10781 ;en_US
dc.relation.ispartofseriesLecture Notes in Computer Science, ; 0302-9743 ; ; 10781 ;en_US
dc.relation.haspart9783319775531.pdfen_US
dc.subjectComputer Scienceen_US
dc.subjectArithmetic and logic units, Computer. ;en_US
dc.subjectData structures (Computer science). ;en_US
dc.subjectAlgorithmsen_US
dc.subjectData Miningen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectComputer Scienceen_US
dc.subjectAlgorithm Analysis and Problem Complexity. ;en_US
dc.subjectArithmetic and Logic Structuen_US
dc.titleGenetic Programmingen_US
dc.title.alternative21st European Conference, EuroGP 2018, Parma, Italy, April 4-6, 2018, Proceedings /en_US
dc.typeBooken_US
dc.publisher.placeCham :en_US
dc.classification.lcQA76.9.A43 ;en_US
Appears in Collections:مهندسی فناوری اطلاعات

Files in This Item:
File Description SizeFormat 
9783319775531.pdf15.07 MBAdobe PDFThumbnail
Preview File