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Genetic Programming Theory and Practice XVII 2020 ed. [Kõva köide]

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  • Formaat: Hardback, 409 pages, kõrgus x laius: 235x155 mm, kaal: 816 g, 112 Illustrations, color; 30 Illustrations, black and white; XXVI, 409 p. 142 illus., 112 illus. in color., 1 Hardback
  • Sari: Genetic and Evolutionary Computation
  • Ilmumisaeg: 08-May-2020
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030399575
  • ISBN-13: 9783030399573
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  • Formaat: Hardback, 409 pages, kõrgus x laius: 235x155 mm, kaal: 816 g, 112 Illustrations, color; 30 Illustrations, black and white; XXVI, 409 p. 142 illus., 112 illus. in color., 1 Hardback
  • Sari: Genetic and Evolutionary Computation
  • Ilmumisaeg: 08-May-2020
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030399575
  • ISBN-13: 9783030399573
Teised raamatud teemal:

These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP.  In this year’s edition, the topics covered include many of the most important issues and research questions in the ?eld, such as: opportune application domains for GP-based methods, game playing and co-evolutionary search, symbolic regression and ef cient learning strategies, encodings and representations for GP, schema theorems, and new selection mechanisms.The volume includes several chapters on best practices and lessons learned from hands-on experience. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results.



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 Efcient 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 Articial
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.