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This paper introduces Minimalist Genetic Programming (MGP), a novel algorithm that replaces evolution with a syntactic derivation process inspired by the Minimalist Program from linguistics, using a MERGE operator to construct symbolic expressions. MGP consistently finds exact ground truth models on symbolic regression tasks where standard GP struggles due to bloat.
This paper demonstrates that large language models trained on code can significantly enhance genetic programming mutation operators, enabling the generation of hundreds of thousands of functional Python programs for robot design in the Sodarace domain without prior training data. The approach, called Evolution through Large Models (ELM), combines LLMs with MAP-Elites to bootstrap new conditional models for context-specific artifact generation.