Abstract
AbstractSymbolic regression, as a regression analysis technique, can find the structure and coefficients of a regression model simultaneously. Genetic programming is an attractive and leading technique for symbolic regression, since it does not require any predefined model structure and has a flexible representation. However, genetic-programming-based symbolic regression (GPSR) often has a poor generalisation ability that hampers its applications to science or industry modelling. In recent years, many researchers have realised the issue and devoted much effort to enhance the generalisation ability of GPSR. This chapter first introduces the generalisation in GPSR and then reviews the state-of-the-art contributions. This chapter also analyses challenges in the area and highlights a number of future directions for interested researchers.
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