Hybrid Symbolic Regression with the Bison Seeker Algorithm
This paper focuses on the use of the Bison Seeker Algorithm (BSA) in a hybrid genetic programming approach for the supervised machine learning method called symbolic regression. While the basic version of symbolic regression optimizes both the model structure and its parameters, the hybrid version can use genetic programming to find the model structure. Consequently, local learning is used to tune model parameters. Such tuning of parameters represents the lifetime adaptation of individuals. This paper aims to compare the basic version of symbolic regression and hybrid version with the lifetime adaptation of individuals via the Bison Seeker Algorithm. Author also investigates the influence of the Bison Seeker Algorithm on the rate of evolution in the search for function, which fits the given input-output data. The results of the current study support the fact that the local algorithm accelerates evolution, even with a few iterations of a Bison Seeker Algorithm with small populations.
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