Minimalist Genetic Programming
Quick Answer
This paper shows that Minimalist Genetic Programming (MGP) redefines genetic programming by framing it as a syntactic derivation task, utilizing a $MERGE$ operator to construct complex structures.
Quick Take
Benchmarked against symbolic regression tasks, MGP consistently outperforms standard GP systems, achieving exact models where traditional methods struggle due to bloat.
Key Points
- MGP uses a binary set formation operator called $MERGE$ for incremental construction.
- The approach is inspired by the Minimalist Program in human language.
- MGP benchmarks show superior performance in symbolic regression tasks.
- Standard GP systems often struggle with bloat in similar tasks.
- MGP's insights into minimalism suggest further exploration in program induction.
Paper Resources
Source Excerpt
arXiv:2606. 10237v1 Announce Type: new Abstract: Genetic programming (GP) is based on two important insights. First, that any learning task can fundamentally be posed as a program induction problem, where the goal is to construct a symbolic hierarchical model that is expressed as a syntax tree. Second, to pose this task as a search problem, and use evolution to locate the desired model. Since it was proposed, GP has produced notable results in a wide range of tasks and problem domains.
This work presents an alternative view by modifying the second core insight of GP, posing the problem as a syntactic derivation task instead. …
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