00000cmm a22000004i 4500 UP-99796217611115695 Buklod 20140211130925.0 m go j cr |n |||auu|a 111031s2011 xxu u | eng d 9781461417705 (eBook) 1461417708 (eBook) (iLib)UPD-00212405634 DLCS DML eng DMLUC QA 76.623 G46 2011eb Genetic programming theory and practice IX [electronic resource] Rick Riolo, Ekaterina Vladislavleva, Jason H. Moore (Eds.). New York Springer c2011. 1 online resource (xxvii, 263 p.) 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. IP-based subscription, access limited to within on-campus computer network. Access via Electronic Resources of the UPD University Library Website. IP-based subscription, access limited to within on-campus computer network. Access via Electronic Resources of the UPD University Library Website. 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. Electronic reproduction. New York SpringerLink 2011. Available via World Wide Web through SpringerLink. Genetic programming (Computer science) Congresses. Electronic books. Riolo, Rick. Vladislavleva, Ekaterina. Moore, Jason H. SpringerLink (Online service). Available for University of the Philippines Diliman via SpringerLink. Click here to access https://doi.org/10.1007/978-1-4614-1770-5 (viewed 15 August 2014) FO Monograph UPD DMLR Electronic Resource