Genetic Programming: Lecture Notes in Computer Science, cartea 13223
Editat de Eric Medvet, Gisele Pappa, Bing Xueen Limba Engleză Paperback – 20 apr 2022
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Specificații
ISBN-13: 9783031020551
ISBN-10: 3031020553
Pagini: 320
Ilustrații: X, 309 p. 83 illus., 65 illus. in color.
Dimensiuni: 155 x 235 x 18 mm
Greutate: 0.49 kg
Ediția:1st edition 2022
Editura: Springer
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science
Locul publicării:Cham, Switzerland
ISBN-10: 3031020553
Pagini: 320
Ilustrații: X, 309 p. 83 illus., 65 illus. in color.
Dimensiuni: 155 x 235 x 18 mm
Greutate: 0.49 kg
Ediția:1st edition 2022
Editura: Springer
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science
Locul publicării:Cham, Switzerland
Cuprins
Long Presentations.- Evolving Adaptive Neural Network Optimizers for Image Classification.- Combining Geometric Semantic GP with Gradient-descent Optimization.- One-Shot Learning of Ensembles of Temporal Logic Formulas for Anomaly Detection in Cyber-Physical Systems.- Multi-objective GP with AWS for Symbolic Regression.- SLUG: Feature Selection Using Genetic Algorithms and Genetic Programming.- Evolutionary Design of Reduced Precision Levodopa-Induced Dyskinesia Classifiers.- Using Denoising Autoencoder Genetic Programming to Control Exploration and Exploitation in Search.- Program Synthesis with Genetic Programming: The Influence of Batch Sizes.- Genetic Programming-Based Inverse Kinematics for Robotic Manipulators.- On the Schedule for Morphological Development of Evolved Modular Soft Robots.- An Investigation of Multitask Linear Genetic Programming for Dynamic Job Shop Scheduling.- Cooperative Co-Evolution and Adaptive Team Composition for a Multi-Rover Resources Allocation Problem.- Short Presentations.- Synthesizing Programs from Program Pieces using Genetic Programming and Refinement Type Checking.- Creating Diverse Ensembles for Classification with Genetic Programming and Neuro-MAP-Elites.- Evolving Monotone Conjunctions in Regimes Beyond Proved Convergence.- Accurate and Interpretable Representations of Environments with Anticipatory Learning Classifier Systems.- Exploiting Knowledge from Code to Guide Program Search.- Multi-Objective Genetic Programming for Explainable Reinforcement Learning.- Permutation-Invariant Representation of Neural Networks with Neuron Embeddings.