- author = "Nam H. Le and Josh Bongard",
- title = "Decoupling Representation and Learning in Genetic Programming: The {LaSER} Approach",
- old_title = "{LASER}: How Learning Can Guide The Evolution Of Equations",
- booktitle = "Genetic Programming Theory and Practice XXII",
- year = "2025",
- editor = "Bogdan Burlacu and Fabricio {Olivetti de Franca} and Alexander Lalejini and Stephen Kelly and Wolfgang Banzhaf",
- series = "Genetic and Evolutionary Computation",
- pages = "283--304",
- address = "Michigan State University, USA",
- month = jun # " 5-7",
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming, LaSER",
- isbn13 = "978-981-95-6397-5",
-
DOI = "
10.1007/978-981-95-6398-2_14",
- abstract = "... are passed to an external learner to model the target task. This approach enables any function approximator, from linear models to neural networks, to serve as a lifetime learner, allowing expressive modeling beyond conventional symbolic forms. We show that LaSER can outperform standard GP, particularly when equipped with nonlinear lifetime learners. While LaSER with linear models (e.g., ridge regression) already offers strong performance on smooth, low-complexity problems, its true advantage emerges as target functions grow in nonlinearity and complexity where nonlinear learners provide a stronger inductive bias. We also present a first attempt at demonstrating an instance of the Baldwin Effect in symbolic regression: under LaSER, evolved representations become increasingly innate, reducing reliance on lifetime learning across generations. By explicitly separating the roles of representation and adaptation, LaSER offers a principled and extensible framework for symbolic modeling tasks.",
-
notes = "lightning talk.
See also \cite{le2025decouplingrepresentationlearninggenetic}
published in 2026 after the workshop",
