Titlebook: Genetic Programming; 25th European Confer Eric Medvet,Gisele Pappa,Bing Xue Conference proceedings 2022 The Editor(s) (if applicable) and T

正論

SLUG: Feature Selection Using Genetic Algorithms and?Genetic ProgrammingThis method is first tested on four regular binary classification datasets, and then on 10 synthetic datasets produced by GAMETES, a tool for embedding epistatic gene-gene interactions into noisy datasets. We compare the results of SLUG with the ones obtained by other GP-based methods that had alrea

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Evolutionary Design of?Reduced Precision Levodopa-Induced Dyskinesia Classifierslevodopa side effects, i.e. levodopa-induced dyskinesia (LID), it is necessary to correctly manage levodopa dosage. This article covers an application of cartesian genetic programming (CGP) to assess LID based on time series collected using accelerators attached to the patient’s body. Evolutionary d

Apogee

Using Denoising Autoencoder Genetic Programming to?Control Exploration and?Exploitation in?Searchhat uses denoising autoencoder long short-term memory networks as a probabilistic model to replace the standard mutation and recombination operators of genetic programming (GP). At each generation, the idea is to flexibly identify promising properties of the parent population and to transfer these p

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Genetic Programming-Based Inverse Kinematics for?Robotic Manipulatorsning of multiple joint parameters of the manipulator to reach a desired position in the Cartesian space. We present a new approach to identify a closed-form solution for the Inverse Kinematics (IK) problem, namely IK-CCGP. The novelty of IK-CCGP is the cooperative coevolutionary learning strategy. U

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