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130626 ||| eng |
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|a 9781461417705
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| 100 |
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|a Riolo, Rick
|e [editor]
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| 245 |
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|a Genetic Programming Theory and Practice IX
|h Elektronische Ressource
|c edited by Rick Riolo, Ekaterina Vladislavleva, Jason H. Moore
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| 250 |
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|a 1st ed. 2011
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| 260 |
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|a New York, NY
|b Springer New York
|c 2011, 2011
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| 300 |
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|a XXVIII, 264 p
|b online resource
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|a What’s in an evolved name? The evolution of modularity via tag-based Reference -- Let the Games Evolve! -- Novelty Search and the Problem with Objectives -- A fine-grained view of phenotypes and locality in genetic programming -- Evolution of an Effective Brain-Computer Interface Mouse via Genetic Programming with Adaptive Tarpeian Bloat Control -- Improved Time Series Prediction and Symbolic Regression with Affine Arithmetic -- Computational Complexity Analysis of Genetic Programming – Initial Results and Future Directions -- Accuracy in Symbolic Regression -- Human-Computer Interaction in a Computational Evolution System for the Genetic Analysis of Cancer -- Baseline Genetic Programming: Symbolic Regression on Benchmarks for Sensory Evaluation Modeling -- Detecting Shadow Economy Sizes With Symbolic Regression -- The Importance of Being Flat – Studying the Program Length Distributions of Operator Equalisation -- FFX: Fast, Scalable, Deterministic Symbolic Regression Technology
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| 653 |
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|a Programming Techniques
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| 653 |
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|a Computer science
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| 653 |
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|a Computer programming
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Algorithms
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Theory of Computation
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| 700 |
1 |
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|a Vladislavleva, Ekaterina
|e [editor]
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| 700 |
1 |
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|a Moore, Jason H.
|e [editor]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
0 |
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|a Genetic and Evolutionary Computation
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| 028 |
5 |
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|a 10.1007/978-1-4614-1770-5
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| 856 |
4 |
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|u https://doi.org/10.1007/978-1-4614-1770-5?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 006.3
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| 520 |
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|a 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. Topics include: modularity and scalability; evolvability; human-competitive results; the need for important high-impact GP-solvable problems;; the risks of search stagnation and of cutting off paths to solutions; the need for novelty; empowering GP search with expert knowledge; In addition, GP symbolic regression is thoroughly discussed, addressing such topics as guaranteed reproducibility of SR; validating SR results, measuring and controlling genotypic complexity; controlling phenotypic complexity; identifying, monitoring, and avoiding over-fitting; finding a comprehensive collection of SR benchmarks, comparing SR to machine learning. This text is for all GP explorers. 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
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