{
  "id": "evoprompt",
  "code": "PTL-0154",
  "term": "EvoPrompt",
  "aliases": [],
  "category": "optimization",
  "definition": "EvoPrompt optimizes prompts with evolutionary algorithms, such as genetic algorithms and differential evolution, in which a language model performs the crossover and mutation steps on a population of prompts.",
  "description": "Using the model as the evolutionary operator keeps every candidate a readable, coherent prompt while borrowing the exploration properties of classical evolutionary search. It needs only API access to the model, not its gradients.",
  "example": null,
  "broader": [
    "prompt-optimization"
  ],
  "narrower": [],
  "related": [
    "promptbreeder",
    "automatic-prompt-engineer"
  ],
  "introduced": 2023,
  "added": "2026-10-10",
  "sources": [
    {
      "title": "EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers",
      "authors": "Guo et al.",
      "year": 2023,
      "url": "https://arxiv.org/abs/2309.08532"
    }
  ],
  "url": "https://protologue.com/t/evoprompt/",
  "citation": "Protologue. (2026). EvoPrompt. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.4.0, PTL-0154). https://protologue.com/t/evoprompt/"
}