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E-raamat: Genetic Programming: 21st European Conference, EuroGP 2018, Parma, Italy, April 4-6, 2018, Proceedings

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Genetic Programming: 21st European Conference, EuroGP 2018, Parma, Italy, April 4-6, 2018, ProceedingsSuurem pilt
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This 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.
Using 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. 
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