Constraining genetic symbolic regression via semantic backpropagation

• Published in: Genetic programming and evolvable machines Vol. 26; no. 1
• Main Authors: Reissmann, Maximilian, Fang, Yuan, Ooi, Andrew S. H., Sandberg, Richard D.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.06.2025; Springer Nature B.V
• Subjects: Artificial Intelligence; Back propagation; Back propagation networks; Biomedical Engineering and Bioengineering; Compilers; Computer Science; Conservation laws; Constraining; Constraints; Electrical Engineering; Gene expression; Genetic algorithms; Interpreters; Mapping; Misalignment; Neural networks; Programming Languages; Programming Techniques; Semantics; Software Engineering/Programming and Operating Systems; Semantic backpropagation; Symbolic regression; Gene expression programming

Evolutionary symbolic regression approaches are powerful tools that can approximate an explicit mapping between input features and observation for various problems. However, ensuring that explored expressions maintain consistency with domain-specific constraints remains a crucial challenge. While ne...