Detailed Explanation: Symbolic regression differs from traditional regression methods by not assuming a predefined model structure. Instead, it uses algorithms, often evolutionary algorithms like genetic programming, to explore a wide range of mathematical expressions and operations. The goal is to find the simplest and most accurate model that describes the underlying data relationships. This approach can discover both the form and the parameters of the model simultaneously, making it highly flexible and capable of revealing complex, non-linear patterns that traditional methods might miss. Applications of symbolic regression span various fields, including physics, biology, finance, and engineering, where it helps uncover fundamental laws and relationships directly from empirical data.

Historical Overview: The concept of symbolic regression dates back to the 1970s, but it gained significant traction in the 1990s with the advent of genetic programming techniques. John Koza's work in the early 1990s was pivotal, as he introduced genetic programming, which became a primary method for performing symbolic regression.

Key Contributors: John Koza is a central figure in the development of symbolic regression, particularly through his pioneering work in genetic programming. His books and research laid the foundation for the widespread use of these techniques. Additionally, contributions from researchers in the fields of evolutionary computation and machine learning have further advanced the algorithms and applications of symbolic regression.