EvoPrompt

A EvoPrompt is an evolutionary prompt optimization technique that connects large language models with evolutionary algorithms for prompt optimization.

• AKA: EvoPrompt Framework, Evolutionary Prompting, LLM-Guided Evolution, Prompt Evolution Framework.
• Context:
  • It can initialize prompt populations using LLM generation and seed prompts.
  • It can perform crossover operations by combining prompt elements from parent prompts.
  • It can execute mutations through LLM-guided variation and semantic perturbation.
  • It can select survivor prompts based on fitness scores and performance metrics.
  • It can leverage LLM understanding to create meaningful variations rather than random changes.
  • It can maintain population diversity through niche preservation and crowding control.
  • It can implement adaptive evolution where mutation rates adjust based on convergence speed.
  • It can utilize multi-objective fitness for prompt quality, length, and robustness.
  • It can track evolutionary lineage to understand successful trait propagation.
  • It can combine global search through evolution with local refinement through LLM editing.
  • ...
  • It can range from being a Basic EvoPrompt to being an Advanced EvoPrompt, depending on its evolutionary operator complexity.
  • It can range from being a Small-Scale EvoPrompt to being a Large-Scale EvoPrompt, depending on its population size.
  • It can range from being a Single-Generation EvoPrompt to being a Multi-Generation EvoPrompt, depending on its evolution duration.
  • It can range from being a Homogeneous EvoPrompt to being a Heterogeneous EvoPrompt, depending on its population diversity.
  • ...
• Example(s):
  • Standard EvoPrompt Implementation, which uses populations of 20-50 prompts evolved over 10-20 generations.
  • EvoPrompt for Classification, which evolves classification prompts for accuracy improvement.
  • EvoPrompt for Generation, which optimizes text generation prompts for quality metrics.
  • Hybrid EvoPrompt, which combines evolution with gradient-based refinement.
  • ...
• Counter-Example(s):
  • Random Prompt Search, which lacks evolutionary pressure and selection mechanism.
  • Single-Point Optimization, which doesn't maintain populations or use genetic operators.
  • Exhaustive Prompt Search, which enumerates rather than evolves prompts.
  • Static Prompt Template, which lacks variation and adaptation.
• See: Evolutionary Prompt Optimization Technique, Genetic Algorithm, LLM-Guided Variation, Crossover Operation, Mutation Operator, Population-Based Search, Fitness Function, Natural Selection, Microsoft APO Framework.

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