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Explore a 19-minute conference talk from ACM SIGPLAN on using data types as a more ergonomic frontend for Grammar-Guided Genetic Programming (GGGP). Delve into the proposed approach of embedding grammar as an internal Domain-Specific Language in the host language framework, offering the same expressive power as BNF and EBNF while leveraging existing tooling. Learn about Meta-Handlers, user-defined overrides of the tree-generation system, and how they extend object-oriented encoding with greater practicability and expressive power. Examine a Python implementation example and compare this approach to textual BNF-representations in terms of expressive power, ergonomics, and performance across 5 benchmarks against PonyGE2. Gain insights into the advantages of this method for improving Genetic Programming applications in Machine Learning, Optimization, and Software Engineering.
