Variable neighborhood programming for symbolic regression

In the field of automatic programming (AP), the solution of a problem is a program, which is usually represented by an AP-tree. A tree is built using functional and terminal nodes. For solving AP problems, we propose a new neighborhood structure that adapts the classical “elementary tree transformat...

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Bibliographic Details
Published in:Optimization letters 2022, Vol.16 (1), p.191-210
Main Authors: Elleuch, Souhir, Jarboui, Bassem, Mladenovic, Nenad, Pei, Jun
Format: Article
Language:English
Subjects:
Computational Intelligence
Mathematics
Mathematics and Statistics
Numerical and Computational Physics
Operations Research/Decision Theory
Optimization
Original Paper
Simulation
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Summary:In the field of automatic programming (AP), the solution of a problem is a program, which is usually represented by an AP-tree. A tree is built using functional and terminal nodes. For solving AP problems, we propose a new neighborhood structure that adapts the classical “elementary tree transformation” (ETT) into this specific AP-tree. The ETT is the process of removing an edge and adding another one to obtain a new feasible tree. Experimental comparison with reduced VNP, i.e., with VNP without local search, genetic programming, and artificial bee colony programming shows clearly advantages of the new proposed BVNP method, in terms of speed of convergence and computational stability.
ISSN:1862-4472
1862-4480
DOI:10.1007/s11590-020-01649-1