Genetic Programming Theory and Practice XVII
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- Artikel-Nr.: 10398034
Beschreibung
1. Characterizing the Effects of Random Subsampling on Lexicase Selection.- 2. It is Time for New Perspectives on How to Fight Bloatin GP.- 3. Explorations of the Semantic Learning Machine Neuroevolution Algorithm.- 4. Can Genetic Programming Perform Explainable Machine Learning for Bioinformatics?.- 5. Symbolic Regression by Exhaustive Search - Reducing the Search Space using Syntactical Constraints and Efficient Semantic Structure Deduplication.- 6. Temporal Memory Sharing in Visual Reinforcement Learning.- 7. The Evolution of Representations in Genetic Programming Trees.- 8. How Competitive is Genetic Programming in Business Data Science Applications?.- 9. Using Modularity Metrics as Design Features to Guide Evolution in Genetic Programming.- 10. Evolutionary Computation and AI Safety.- 11. Genetic Programming Symbolic Regression.- 12. Hands-on Artificial Evolution through Brain Programming.- 13. Comparison of Linear Genome Representations For Software Synthesis.- 14. Enhanced Optimization with Composite Objectives and Novelty Pulsation.- 15. New Pathways in Coevolutionary Computation.- 16. 2019 Evolutionary Algorithms Review.- 17. Evolving a Dota 2 Hero Bot with a Probabilistic Shared Memory Model.- 18. Modelling Genetic Programming as a Simple Sampling Algorithm.- 19. An Evolutionary System for Better Automatic Software Repair.- Index.
Eigenschaften
Breite: | 159 |
Gewicht: | 802 g |
Höhe: | 242 |
Länge: | 30 |
Seiten: | 409 |
Sprachen: | Englisch |
Autor: | Bill Worzel, Erik Goodman, Leigh Sheneman, Leonardo Trujillo, Wolfgang Banzhaf |
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