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About this paper
Nam H. Le, Josh Bongard
The LaSER framework introduces a method for Genetic Programming (GP) that separates the evolution of symbolic representations from their task-specific learning, allowing an external supervised learner to map GP-generated features to targets. This approach demonstrates enhanced generalization and robustness on symbolic regression benchmarks compared to standard GP.
Key insights
- Decoupling representation evolution from lifetime learning in GP offers a principled approach to address its inherent limitations, drawing inspiration from modern machine learning.
- The LaSER framework generalizes Keijzer's linear scaling, demonstrating its broader applicability as a specific instance of a more general approach.
- LaSER provides a computational testbed for studying the Baldwin Effect, showing how learned adaptations can influence and guide evolutionary processes toward "innate" solutions.
Summary by alphaXiv.
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