Choosing function sets with better generalisation performance for symbolic regression models
Volume: 22, Issue: 1, Pages: 73 - 100
Published: May 12, 2020
Abstract
Supervised learning by means of Genetic Programming (GP) aims at the evolutionary synthesis of a model that achieves a balance between approximating the target function on the training data and generalising on new data. The model space searched by the Evolutionary Algorithm is populated by compositions of primitive functions defined in a function set. Since the target function is unknown, the choice of function set’s constituent elements is...
Paper Details
Title
Choosing function sets with better generalisation performance for symbolic regression models
Published Date
May 12, 2020
Volume
22
Issue
1
Pages
73 - 100
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