Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication

Understand

Symbolic regression is a powerful system identification technique in industrial scenarios where no prior knowledge on model structure is available.

• Such scenarios often require specific model properties such as interpretability, robustness, trustworthiness and plausibility, that are not easily achievable using standard approaches like genetic programming for symbolic regression.
• In this chapter we introduce a deterministic symbolic regression algorithm specifically designed to address these issues.
• The algorithm uses a context-free grammar to produce models that are parameterized by a non-linear least squares local optimization procedure.