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Variable Neighborhood Programming as a Tool of Machine Learning

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Part of the Springer Optimization and Its Applications book series (SOIA,volume 170)

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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Acknowledgements

This publication is partially supported by the Khalifa University of Science and Technology under Award No. RC2 DSO. This research is also partially supported by the framework of Grant BR05236839, development of information technologies and systems for stimulation of personality’s sustainable development as one of the bases of development of digital Kazakhstan.

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Correspondence to Nenad Mladenovic .

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Mladenovic, N., Jarboui, B., Elleuch, S., Mussabayev, R., Rusetskaya, O. (2021). Variable Neighborhood Programming as a Tool of Machine Learning. In: Pardalos, P.M., Rasskazova, V., Vrahatis, M.N. (eds) Black Box Optimization, Machine Learning, and No-Free Lunch Theorems. Springer Optimization and Its Applications, vol 170. Springer, Cham. https://doi.org/10.1007/978-3-030-66515-9_9

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