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Symbolic Regression Methods

In: Machine Learning Control by Symbolic Regression

Author

Listed:
  • Askhat Diveev

    (Russian Academy of Sciences (FRC CSC RAS), Federal Research Center “Computer Science and Control”)

  • Elizaveta Shmalko

    (Russian Academy of Sciences (FRC CSC RAS), Federal Research Center “Computer Science and Control”)

Abstract

This chapter provides a detailed description of different symbolic regression methods. Some methods differ directly in the form of coding, as well as variational methods are based on the principle of small variations of the basic solution. By analogy with deep learning, the technology of the multilayer symbolic regression method is presented. We deliberately did not include detailed historical references in the description of the methods, focusing only on practically significant entities. The description of each method includes the encoding procedures with examples and the main features of the searching algorithm for finding the optimal solution in the code space with an emphasis on the implementation of the crossover operation of the genetic algorithm, which differs depending on the type of encoding. We do not pretend to provide a comprehensive overview of symbolic regression methods, but present only those symbolic regression methods that have already been applied in machine learning control problems, or we managed to apply them to the class of machine learning problems under consideration. As new symbolic regression methods appear for solving machine learning control problems, we will be happy to supplement the presented description.

Suggested Citation

  • Askhat Diveev & Elizaveta Shmalko, 2021. "Symbolic Regression Methods," Springer Books, in: Machine Learning Control by Symbolic Regression, chapter 0, pages 55-104, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-83213-1_4
    DOI: 10.1007/978-3-030-83213-1_4
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