Minimalist Genetic Programming: A New Approach to Program Induction Using Linguistic Theory
Researchers have proposed Minimalist Genetic Programming (MGP), a new algorithm that replaces the evolutionary search mechanism of traditional genetic programming with a process inspired by Noam Chomsky's Minimalist Program in linguistics. Unlike standard genetic programming, MGP frames program induction as a syntactic derivation task, using a binary set-formation operator called MERGE to incrementally build symbolic expressions. The approach shows promise for reducing 'bloat'—a longstanding problem in genetic programming—and consistently recovers exact ground-truth models on symbolic regression benchmarks where standard methods struggle.
Genetic programming (GP) has long been used to automatically construct symbolic models by treating learning as a program induction problem solved through evolutionary search. The newly proposed Minimalist Genetic Programming (MGP), submitted to arXiv by Leonardo Trujillo and colleagues, retains GP's core idea of building hierarchical syntax-tree models but replaces evolutionary search with a Markovian derivation process borrowed from minimalist linguistics. The key computational primitive is MERGE, a binary set-formation operator from Chomsky's Minimalist Program, which incrementally assembles complex symbolic structures from atomic building blocks called a lexicon. This design allows MGP to identify and reuse core sub-expressions, addressing the bloat problem—uncontrolled growth of program size—that plagues standard GP on difficult symbolic regression tasks. Benchmarking results indicate that, given an appropriate lexicon of atomic syntactic objects, MGP reliably recovers exact ground-truth mathematical models in cases where standard GP fails to do so. The authors argue that insights from minimalist linguistics are broadly relevant to program induction and warrant further investigation. The work is currently a preprint and has not yet undergone formal peer review.
What's missing
As a preprint, the paper has not yet been peer-reviewed. Key open questions include: how sensitive MGP's performance is to the choice of lexicon (and how that lexicon is selected or learned), how the method scales to higher-dimensional or real-world symbolic regression problems beyond the benchmarks tested, and how MGP compares to other bloat-control techniques such as size-limited GP or grammar-guided GP. The paper's scope appears limited to symbolic regression, leaving generalization to other program induction tasks undemonstrated.
What different sources said
- arXiv cs.LGCenter
Minimalist Genetic Programming
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