Minimalist Genetic Programming

The paper introduces Minimalist Genetic Programming (MGP), a novel algorithm that reinterprets genetic programming by framing it as a syntactic derivation task rather than an evolutionary search problem. MGP utilizes a binary set formation operator called $MERGE$ to incrementally construct complex syntactic structures and demonstrates superior performance on challenging symbolic regression tasks, consistently producing exact ground truth models where traditional GP fails due to bloat. This approach highlights the relevance of linguistic minimalism to program induction, suggesting new avenues for research in symbolic model construction.