Evolutionary Prompt Optimization Technique

A Evolutionary Prompt Optimization Technique is a prompt optimization technique that optimizes prompts using genetic algorithm-style mutations, crossovers, and selection.

• AKA: Evolutionary Prompt Optimization, Genetic Prompt Optimization, Population-Based Prompt Search, Prompt Evolution Method.
• Context:
  • It can perform prompt mutations with LLM-guided variations and random perturbations.
  • It can execute crossover operations with element combination and trait mixing.
  • It can select fittest prompts with performance-based survival and tournament selection.
  • It can maintain population diversity through mutation rates and selection pressure.
  • It can implement generation cycles with reproduction, variation, and selection phases.
  • It can utilize fitness functions based on task performance and evaluation metrics.
  • It can apply elitism strategy to preserve best-performing prompts across generations.
  • It can incorporate adaptive mutation based on population convergence and fitness plateaus.
  • It can leverage multi-objective optimization for prompt quality and computational efficiency.
  • It can use hybrid approaches combining evolutionary search with local optimization.
  • It can track genealogy to understand successful traits and inheritance patterns.
  • ...
  • It can range from being a Basic Evolutionary Prompt Optimization Technique to being an Advanced Evolutionary Prompt Optimization Technique, depending on its algorithm sophistication.
  • It can range from being a Small-Population Evolutionary Prompt Optimization Technique to being a Large-Population Evolutionary Prompt Optimization Technique, depending on its population size.
  • It can range from being a Single-Objective Evolutionary Prompt Optimization Technique to being a Multi-Objective Evolutionary Prompt Optimization Technique, depending on its optimization goals.
  • It can range from being a Steady-State Evolutionary Prompt Optimization Technique to being a Generational Evolutionary Prompt Optimization Technique, depending on its replacement strategy.
  • ...
• Example(s):
  • EvoPrompt, which connects LLMs with evolutionary algorithms for prompt optimization.
  • AutoPrompt, which uses population-based search with mutations and crossovers.
  • Genetic Programming for Prompts, which evolves prompt structures using tree representation.
  • Differential Evolution for Prompts, which uses vector differences for prompt variation.
  • ...
• Counter-Example(s):
  • Gradient-Based Prompt Optimization Technique, which uses gradient descent rather than population evolution.
  • Meta-Prompting Framework, which uses LLM reasoning rather than evolutionary search.
  • Bayesian Prompt Optimization, which uses probabilistic models rather than populations.
  • Grid Search Prompt Optimization, which uses exhaustive search rather than evolution.
• See: Prompt Optimization Technique, Evolutionary Algorithm, Genetic Algorithm, Population-Based Search, Prompt Mutation Operator, Crossover Operation, Fitness Function, Selection Mechanism, Microsoft APO LLM Prompt Optimization Method, Natural Selection Algorithm.

---