- [I]. Long presentations: 1. Ariadne : evolving test data using grammatical evolution / Muhammad Sheraz Anjum, Conor Ryan
- 2. Quantum program synthesis : swarm algorithms and benchmarks / Timothy Atkinson, Athena Karsa, John Drake, Jerry Swan
- 3. A genetic programming approach to predict mosquitoes abundance / Riccardo Gervasi, Irene Azzali, Donal Bisanzio, Andrea Mosca, Luigi Bertolotti, Mario Giacobini
- 4. Complex network analysis of a genetic programming phenotype network / Ting Hu, Marco Tomassini, Wolfgang Banzhof
- 5. Improving genetic programming with novel exploration : exploitation control / Jonathan Kelly, Erik Hemberg, Una-May O'Reilly
- 6. Towards a scalable EA-based optimization of digital circuits / Jitka Kocnova, Zdenek Vasicek
- 7. Cartesian genetic programming as an optimizer of programs evolved with geometric semantic genetic programming / Ondrej Koncal, Lukas Sekanina
- 8. Can genetic programming do manifold learning too? / Andrew Lensen, Bing Xue, Mengjie Zhang
- 9. Why is auto-encoding difficult for genetic programming? / James McDermott
- 10. Solution and fitness evolution (SAFE) : coevolving solutions and their objective functions / Moshe Sipper, James H. Moore, Ryan J. Urbanowicz
- 11. A model of external memory for navigation in partially observable visual reinforcement learning tasks / Robert J. Smith, Malcolm I. Heywood
- 12. Fault detection and classification for induction motors using genetic programming / Yu Zhang, Ting Hu, Xiaodong Liang, Mohammad Zawad Ali, Md. Nasmus Sakib Khan Shabbir.
- [II]. Short presentations: 13. Fast DENSER : efficient deep NeuroEvolution / Filipe Assunção, Nuno Lourenço, Penousal Machado, Bernardete Ribeiro
- 14. A vectorial approach to genetic programming / Irene Azzali, Leonardo Vanneschi, Sara Silva, Illya Bakurov, Mario Giacobini
- 15. Comparison of genetic programming methods on design of cryptographic Boolean functions / Jakub Husa
- 16. Evolving AVX512 parallel C code using GP / William B. Langdon, Ronny Lorenz
- 17. Hyper-bent Boolean functions and evolutionary algorithms / Luca Mariot, Domagoj Jakobovic, Alberto Leporati, Stjepan Picek
- 18. Learning class disjointness axioms using grammatical evolution / Thu Huong Nguyen, Andrea G.B. Tettamanzi.
This book constitutes the refereed proceedings of the 22nd European Conference on Genetic Programming, EuroGP 2019, held as part of Evo* 2019, in Leipzig, Germany, in April 2019, co-located with the Evo* events EvoCOP, EvoMUSART, and EvoApplications. The 12 revised full papers and 6 short papers presented in this volume were carefully reviewed and selected from 36 submissions. They cover a wide range of topics and reflect the current state of research in the field. With a special focus on real-world applications in 2019, the papers are devoted to topics such as the test data design in software engineering, fault detection and classification of induction motors, digital circuit design, mosquito abundance prediction, machine learning and cryptographic function design. .
(source: Nielsen Book Data)
