Cantitate/Preț
Produs

Genetic Programming: Lecture Notes in Computer Science, cartea 12101

Editat de Ting Hu, Nuno Lourenço, Eric Medvet, Federico Divina
en Limba Engleză Paperback – 13 mar 2020
This book constitutes the refereed proceedings of the 23rd European Conference on Genetic Programming, EuroGP 2020, held as part of Evo*2020, in Seville, Spain, in April 2020, co-located with the Evo*2020 events EvoCOP, EvoMUSART and EvoApplications.
The 12 full papers and 6 short papers presented in this book were carefully reviewed and selected from 36 submissions. The papers cover a wide spectrum of topics, including designing GP algorithms for ensemble learning, comparing GP with popular machine learning algorithms, customising GP algorithms for more explainable AI applications to real-world problems.
Citește tot Restrânge

Din seria Lecture Notes in Computer Science

Preț: 32259 lei

Preț vechi: 40323 lei
-20%

Puncte Express: 484

Carte tipărită la comandă

Livrare economică 28 iulie-11 august

Livrare prin curier în România Termenul estimat este afișat lângă disponibilitate.
Transport gratuit de la 40000 lei Plată online sau ramburs, în funcție de opțiunile comenzii.
Retur gratuit în 14 zile Comandă securizată și suport în română.

Specificații

ISBN-13: 9783030440930
ISBN-10: 3030440931
Pagini: 308
Ilustrații: X, 295 p. 157 illus., 72 illus. in color.
Dimensiuni: 155 x 235 x 17 mm
Greutate: 0.47 kg
Ediția:1st edition 2020
Editura: Springer
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

Hessian Complexity Measure for Genetic Programming-based Imputation Predictor Selection in Symbolic Regression with Incomplete Data.- Seeding Grammars in Grammatical Evolution to Improve Search Based Software Testing.- Incremental Evolution and Development of Deep Artificial Neural Networks.- Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming.- Comparing Genetic Programming Approaches for Non-Functional Genetic Improvement.- Automatically Evolving Lookup Tables for Function Approximation.- Optimising Optimisers with Push GP.- An Evolutionary View on Reversible Shift-invariant Transformations.- Benchmarking Manifold Learning Methods on a Large Collection of Datasets.- Ensemble Genetic Programming.- SGP-DT: Semantic Genetic Programming Based on Dynamic Targets.- Effect of Parent Selection Methods on Modularity.- Time Control or Size Control? Reducing Complexity and Improving Accuracy of Genetic Programming Models.- Challenges of Program Synthesis withGrammatical Evolution.- Detection of Frailty Using Genetic Programming : The Case of Older People in Piedmont, Italy.- Is k Nearest Neighbours Regression Better than GP.- Guided Subtree Selection for Genetic Operators in Genetic Programming for Dynamic Flexible Job Shop Scheduling.- Classification of Autism Genes using Network Science and Linear Genetic Programming.