Minimalist Genetic Programming: A New Approach to Program Induction Using Linguistic Theory
Researchers propose Minimalist Genetic Programming (MGP), a new algorithm for program induction that replaces evolutionary search with principles from linguistic theory, specifically the Minimalist Program's MERGE operator. The approach addresses a known problem in standard genetic programming called bloat, where evolved programs become unnecessarily complex. MGP demonstrates improved performance on symbolic regression tasks, suggesting that insights from human language structure can improve machine learning algorithms.
This arXiv paper introduces Minimalist Genetic Programming, which reimagines genetic programming by substituting evolutionary search with computational principles derived from Noam Chomsky's Minimalist Program in linguistics. Rather than using evolution to search for symbolic models expressed as syntax trees, MGP employs a binary set formation operator called MERGE to incrementally construct complex syntactic structures through a simple Markovian process. The authors benchmark their approach on symbolic regression tasks where standard genetic programming typically struggles due to program bloat—the tendency for evolved solutions to become unnecessarily complex. Results indicate that when appropriate atomic building blocks are selected, MGP consistently discovers exact ground-truth models on test cases where conventional GP fails. The work suggests that principles from human language structure, which minimalism posits as an optimal solution for linking cognitive systems, may offer valuable insights for improving program induction algorithms.
What's missing
The paper does not provide detailed computational complexity analysis comparing MGP to standard GP, nor does it discuss scalability to larger or more diverse problem domains beyond symbolic regression. The study's own limitations regarding the dependence on proper lexicon selection and the generalizability of the approach to non-symbolic tasks remain open questions.
What different sources said
- arXiv cs.LGCenter
Minimalist Genetic Programming
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