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A Survey of Statistical Machine Learning Elements in Genetic Programming

Modern genetic programming (GP) operates within the statistical machine learning (SML) framework. In this framework, evolution needs to balance between approximation of an unknown target function on the training data and generalization, which is the ability to predict well on new data. This paper provides a survey and critical discussion of SML methods that enable GP to generalize.

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Judul Seri

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION; Vol.23 Issue 6 December 2019

No. Panggil

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Penerbit

: .,

Deskripsi Fisik

p. 1029 - 1048

Bahasa

English

ISBN/ISSN

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Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

MODEL SELECTION
CLASSIFICATION
REGULARIZATION
GENERALIZATION
OVERFITTING
SYMBOLIC REGRESSION
BIAS-VARIANCE TRADEOFF
STATISTICAL MACHINE LEARNING (SML)

Info Detail Spesifik

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Pernyataan Tanggungjawab

Alexandros Agapitos, Roisin Loughran, Miguel Nicolau, Simon Lucas, Michael O’Neill, Anthony Brabazon

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