Genetic Programming

Description

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. Front Matter ....Pages I-XII Front Matter ....Pages 1-1 Using GP Is NEAT: Evolving Compositional Pattern Production Functions (Filipe Assunção, Nuno Lourenço, Penousal Machado, Bernardete Ribeiro)....Pages 3-18 Evolving the Topology of Large Scale Deep Neural Networks (Filipe Assunção, Nuno Lourenço, Penousal Machado, Bernardete Ribeiro)....Pages 19-34 Evolving Graphs by Graph Programming (Timothy Atkinson, Detlef Plump, Susan Stepney)....Pages 35-51 Pruning Techniques for Mixed Ensembles of Genetic Programming Models (Mauro Castelli, Ivo Gonçalves, Luca Manzoni, Leonardo Vanneschi)....Pages 52-67 Analyzing Feature Importance for Metabolomics Using Genetic Programming (Ting Hu, Karoliina Oksanen, Weidong Zhang, Edward Randell, Andrew Furey, Guangju Zhai)....Pages 68-83 Generating Redundant Features with Unsupervised Multi-tree Genetic Programming (Andrew Lensen, Bing Xue, Mengjie Zhang)....Pages 84-100 On the Automatic Design of a Representation for Grammar-Based Genetic Programming (Eric Medvet, Alberto Bartoli)....Pages 101-117 Multi-level Grammar Genetic Programming for Scheduling in Heterogeneous Networks (Takfarinas Saber, David Fagan, David Lynch, Stepan Kucera, Holger Claussen, Michael O’Neill)....Pages 118-134 Scaling Tangled Program Graphs to Visual Reinforcement Learning in ViZDoom (Robert J. Smith, Malcolm I. Heywood)....Pages 135-150 Towards in Vivo Genetic Programming: Evolving Boolean Networks to Determine Cell States (Nadia S. Taou, Michael A. Lones)....Pages 151-165 A Multiple Expression Alignment Framework for Genetic Programming (Leonardo Vanneschi, Kristen Scott, Mauro Castelli)....Pages 166-183 Front Matter ....Pages 185-185 Multi-objective Evolution of Ultra-Fast General-Purpose Hash Functions (David Grochol, Lukas Sekanina)....Pages 187-202 A Comparative Study on Crossover in Cartesian Genetic Programming (Jakub Husa, Roman Kalkreuth)....Pages 203-219 Evolving Better RNAfold Structure Prediction (William B. Langdon, Justyna Petke, Ronny Lorenz)....Pages 220-236 Geometric Crossover in Syntactic Space (João Macedo, Carlos M. Fonseca, Ernesto Costa)....Pages 237-252 Investigating a Machine Breakdown Genetic Programming Approach for Dynamic Job Shop Scheduling (John Park, Yi Mei, Su Nguyen, Gang Chen, Mengjie Zhang)....Pages 253-270 Structurally Layered Representation Learning: Towards Deep Learning Through Genetic Programming (Lino Rodriguez-Coayahuitl, Alicia Morales-Reyes, Hugo Jair Escalante)....Pages 271-288 Comparing Rule Evaluation Metrics for the Evolutionary Discovery of Multi-relational Association Rules in the Semantic Web (Minh Duc Tran, Claudia d’Amato, Binh Thanh Nguyen, Andrea G. B. Tettamanzi)....Pages 289-305 Genetic Programming Hyper-Heuristic with Cooperative Coevolution for Dynamic Flexible Job Shop Scheduling (Daniel Yska, Yi Mei, Mengjie Zhang)....Pages 306-321 Back Matter ....Pages 323-323