EvoPrompt
Connects evolutionary algorithms (Genetic Algorithm and Differential Evolution) with an LLM crossover/mutation operator scored on validation sets.
The loop
- Proposes
An LLM performs evolutionary crossover and differential mutation on parent discrete instructions to generate child candidates.
- Judged by
A deterministic evaluation harness scores each child candidate across a development dataset and computes exact task metrics.
- Steers
Roulette-wheel selection and population elitism keep top-scoring candidates for the next evolutionary generation.
Headline result
Outperformed human-engineered and gradient-free prompt baselines by up to 25% across 31 NLP and reasoning datasets.
What this does not prove
Can overfit to a small development split if validation examples do not cover the test distribution.
How it generalizes the harness
A Genetic Algorithm or Differential Evolution loop controls population selection and calls the LLM strictly as a semantic crossover/mutation operator.
Classification
| What it proposes | Heuristic |
|---|---|
| Generative model | GPT-3.5 / Alpaca evolutionary crossover operator |
| Algorithmic authority | Genetic Algorithm / Differential Evolution controller and benchmark evaluator |
| Feedback | Score |
| What gets updated | Population of candidates |
| When it runs | Offline, at design time |
| Validity | Empirical (measured) |
| Evidence | Code released |
| Lineage | Original system |