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Variable Neighborhood Programming as a Tool of Machine Learning

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

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

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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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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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Authors and Affiliations

  1. Department of Industrial and Systems Engineering, Research Center on Digital Supply Chain and Operations Management, Khalifa University, Abu Dhabi, UAE

    Nenad Mladenovic

  2. Higher Colleges of Technology, Abu Dhabi, UAE

    Bassem Jarboui

  3. Department of Management Information Systems, College of Business and Economics, Qassim University, Buraydah, Saudi Arabia

    Souhir Elleuch

  4. Institute of Information and Computational Technologies, Almata, Kazakhstan

    Rustam Mussabayev

  5. Leontief Centre, Saint Petersburg State University of Economics, Saint Petersburg, Russia

    Olga Rusetskaya

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  1. Nenad Mladenovic
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  2. Bassem Jarboui
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  3. Souhir Elleuch
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  4. Rustam Mussabayev
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Correspondence to Nenad Mladenovic .

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  1. Department of Industrial & Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  2. Moscow Aviation Institute, Moscow, Russia

    Varvara Rasskazova

  3. Mathematics Department, University of Patras, Patras, Greece

    Michael N. Vrahatis

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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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