- Intro; Preface; Organization; Contents; Oral Presentations; Evolutionary Program Sketching; 1 Introduction; 2 Program Sketching; 3 Evolutionary Program Sketching; 3.1 Problem Specification; 3.2 Instruction Set; 3.3 Fitness Function; 3.4 Exploiting the Feedback from Hole Completion; 4 Related Work; 5 Experimental Evaluation; 6 Discussion; 7 Conclusion; References; Exploring Fitness and Edit Distance of Mutated Python Programs; 1 Introduction; 2 Related Work; 3 Our Implementation of Genetic Improvement; 3.1 Fitness Function; 3.2 Search Algorithm; 4 Experimental Setup
- 4.1 Description of the Programs Targeted by GI5 Results; 5.1 Change in Fitness; 5.2 Average Fitness with Respect to Edit List Size; 5.3 Discrete Steps in Fitness; 6 Conclusions; References; Differentiable Genetic Programming; 1 Introduction; 2 Program Encoding; 3 The Algebra of Truncated Polynomials; 3.1 The Link to Taylor Polynomials; 3.2 Non Rational Functions; 4 Example of a dCGP; 5 Learning Constants in Symbolic Regression; 5.1 Ephemeral Constants Approach; 5.2 Weighted dCGP Approach; 6 Solution to Differential Equations; 7 Discovery of Prime Integrals; 8 Conclusions; References
- Evolving Game State Features from Raw Pixels1 Introduction; 2 Related Research; 3 Materials; 3.1 Games; 3.2 Handcrafted Game State Features; 4 Evolving Video Game State Visual Features Using Genetic Programming; 4.1 Evolving Game State Features; 4.2 Voting for Actions; 5 Results; 6 Conclusion; References; Emergent Tangled Graph Representations for Atari Game Playing Agents; 1 Introduction; 2 Background; 3 The Arcade Learning Environment; 3.1 Screen State Space Representation; 4 Evolving Tangled Program Graphs; 4.1 Coevolving Teams of Programs; 4.2 Emergent Modularity
- 4.3 Diversity Maintenance5 Empirical Experiments; 5.1 Experimental Setup; 5.2 Results; 5.3 Solution Analysis; 6 Conclusion and Future Work; References; A General Feature Engineering Wrapper for Machine Learning Using -Lexicase Survival; 1 Introduction; 2 Feature Engineering Wrapper; 2.1 -lexicase Survival; 2.2 Scaling; 3 Related Work; 4 Experimental Analysis; 4.1 Problems; 5 Results; 5.1 Hyper-Parameter Optimization; 5.2 Problem Performance; 5.3 Statistical Analysis; 6 Discussion; 7 Conclusions; References; Visualising the Search Landscape of the Triangle Program; 1 Genetic Improvement
- 2 Triangle Program Software Engineering Benchmark3 Binary Representation: Replacing Comparisons with One Alternative; 3.1 High Order Binary Schema Are Not Deceptive; 3.2 Binary Schema Predict All Solutions of the Triangle Program; 3.3 Local Search Landscape of the Binary Space; 4 Original All Comparisons; 4.1 Fitness Space of Triangle Program; 4.2 High Order Schema Analysis; 4.3 Local Search for the Triangle Program; 4.4 Local Optima Networks; 5 Conclusions; References; RANSAC-GP: Dealing with Outliers in Symbolic Regression with Genetic Programming; 1 Introduction; 2 Background; 2.1 Outliers
This book constitutes the refereed proceedings of the 20th European Conference on Genetic Programming, EuroGP 2017, held in Amsterdam, The Netherlands, in April 2017, co-located with the Evo* 2017 events, EvoCOP, EvoMUSART, and EvoApplications. The 14 revised full papers presented together with 8 poster papers were carefully reviewed and selected from 32 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 program synthesis, genetic improvement, grammatical representations, self-adaptation, multi-objective optimisation, program semantics, search landscapes, mathematical programming, games, operations research, networks, evolvable hardware, and program synthesis benchmarks.
(source: Nielsen Book Data)
