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Variable Neighborhood Programming as a Tool of Machine Learning

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

Author

Listed:
  • Nenad Mladenovic

    (Khalifa University)

  • Bassem Jarboui

    (Higher Colleges of Technology)

  • Souhir Elleuch

    (Qassim University)

  • Rustam Mussabayev

    (Institute of Information and Computational Technologies)

  • Olga Rusetskaya

    (Saint Petersburg State University of Economics)

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.

Suggested Citation

  • Nenad Mladenovic & Bassem Jarboui & Souhir Elleuch & Rustam Mussabayev & Olga Rusetskaya, 2021. "Variable Neighborhood Programming as a Tool of Machine Learning," Springer Optimization and Its Applications, in: Panos M. Pardalos & Varvara Rasskazova & Michael N. Vrahatis (ed.), Black Box Optimization, Machine Learning, and No-Free Lunch Theorems, pages 221-271, Springer.
    • Handle: RePEc:spr:spochp:978-3-030-66515-9_9
    DOI: 10.1007/978-3-030-66515-9_9
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