Genetic Programming [electronic resource] : 23rd European Conference, EuroGP 2020, Held as Part of EvoStar 2020, Seville, Spain, April 15–17, 2020, Proceedings /

Hessian Complexity Measure for Genetic Programming-based Imputation Predictor Selection in Symbolic Regression with Incomplete Data -- Seeding Grammars in Grammatical Evolution to Improve Search Based Software Testing -- Incremental Evolution and Development of Deep Artificial Neural Networks -- Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming -- Comparing Genetic Programming Approaches for Non-Functional Genetic Improvement -- Automatically Evolving Lookup Tables for Function Approximation -- Optimising Optimisers with Push GP -- An Evolutionary View on Reversible Shift-invariant Transformations -- Benchmarking Manifold Learning Methods on a Large Collection of Datasets -- Ensemble Genetic Programming -- SGP-DT: Semantic Genetic Programming Based on Dynamic Targets -- Effect of Parent Selection Methods on Modularity -- Time Control or Size Control? Reducing Complexity and Improving Accuracy of Genetic Programming Models -- Challenges of Program Synthesis withGrammatical Evolution -- Detection of Frailty Using Genetic Programming : The Case of Older People in Piedmont, Italy -- Is k Nearest Neighbours Regression Better than GP -- Guided Subtree Selection for Genetic Operators in Genetic Programming for Dynamic Flexible Job Shop Scheduling -- Classification of Autism Genes using Network Science and Linear Genetic Programming.

This book constitutes the refereed proceedings of the 23rd European Conference on Genetic Programming, EuroGP 2020, held as part of Evo*2020, in Seville, Spain, in April 2020, co-located with the Evo*2020 events EvoCOP, EvoMUSART and EvoApplications. The 12 full papers and 6 short papers presented in this book were carefully reviewed and selected from 36 submissions. The papers cover a wide spectrum of topics, including designing GP algorithms for ensemble learning, comparing GP with popular machine learning algorithms, customising GP algorithms for more explainable AI applications to real-world problems.