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Erschienen in:
Learning Enabled Constrained Black-Box Optimization Kapitel lesen Erstes Kapitel lesen

2021 | OriginalPaper | Buchkapitel

Variable Neighborhood Programming as a Tool of Machine Learning

verfasst von : Nenad Mladenovic, Bassem Jarboui, Souhir Elleuch, Rustam Mussabayev, Olga Rusetskaya

Erschienen in: Black Box Optimization, Machine Learning, and No-Free Lunch Theorems

Verlag: Springer International Publishing

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Abstract

Automatic programming is an efficient technique that has contributed to an important development in the artificial intelligence and machine learning fields. In this chapter, we introduce the technique called Variable Neighborhood Programming (VNP) that was inspired by the principle of the Variable Neighborhood Search (VNS) algorithm. VNP starts from a single solution presented by a program, and the search for a good quality global solution (program) continues by exploring different neighborhoods. The goal of our algorithm is to generate a good representative program adequate to a selected problem. VNP takes the advantages of the systematic change of neighborhood structures randomly or within a local search algorithm to diversify or intensify search through the solution space. To show its efficiency and usefulness, the VNP method is applied first for solving the symbolic regression problem (VNP-SRP) and tested and compared on usual test instances from the literature. In addition, the VNP-SRP method is tested in finding formulas for life expectancy as a function of some health care economic factors in 18 Russian districts. Finally, the VNP is implemented on prediction and classification problems and tested on real-life maintenance railway problems from the US railway system.

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Vorheriges Kapitel Optimization Under Uncertainty Explains Empirical Success of Deep Learning Heuristics
Nächstes Kapitel Non-lattice Covering and Quantization of High Dimensional Sets
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Metadaten
Titel
Variable Neighborhood Programming as a Tool of Machine Learning
verfasst von
Nenad Mladenovic
Bassem Jarboui
Souhir Elleuch
Rustam Mussabayev
Olga Rusetskaya
Copyright-Jahr
2021
Buch
Black Box Optimization, Machine Learning, and No-Free Lunch Theorems

Print ISBN: 978-3-030-66514-2

Electronic ISBN: 978-3-030-66515-9

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-66515-9_9

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