Evolutionary Prompt Optimization Algorithm

A Evolutionary Prompt Optimization Algorithm is a prompt optimization algorithm that optimizes prompts using genetic algorithm principles including mutation, crossover, and selection operations.

• AKA: Genetic Prompt Optimization Algorithm, Population-Based Prompt Search Algorithm, Evolutionary Prompt Search Algorithm, GA-Based Prompt Optimization Algorithm.
• Context:
  • It can typically perform prompt mutations with LLM-guided variations maintaining semantic validity.
  • It can typically execute crossover operations with element combination creating hybrid prompts.
  • It can typically implement fitness evaluation using task-specific metrics for prompt selection.
  • It can typically maintain population diversity through mutation rates and selection pressure.
  • It can often select fittest prompts with performance-based survival using tournament selection.
  • It can often incorporate elitism strategy preserving best-performing prompts across generations.
  • It can often apply adaptive mutation rates responding to population convergence patterns.
  • It can often achieve optimal solutions within multiple generations for typical optimization tasks.
  • It can range from being a Basic Evolutionary Prompt Optimization Algorithm to being an Advanced Population-Based Evolutionary Prompt Optimization Algorithm, depending on its algorithmic complexity.
  • It can range from being a Single-Objective Evolutionary Prompt Optimization Algorithm to being a Multi-Objective Evolutionary Prompt Optimization Algorithm, depending on its optimization goals.
  • It can range from being a Steady-State Evolutionary Prompt Optimization Algorithm to being a Generational Evolutionary Prompt Optimization Algorithm, depending on its replacement strategy.
  • It can range from being a Local Evolutionary Prompt Optimization Algorithm to being a Global Evolutionary Prompt Optimization Algorithm, depending on its search scope.
  • ...
• Examples:
  • Core Evolutionary Implementations, such as:
    • EvoPrompt Algorithm connecting LLMs with evolutionary algorithms.
    • AutoPrompt Algorithm using systematic mutations and crossovers.
    • Genetic Prompt Search Algorithm implementing standard GA operations.
  • Hybrid Evolutionary Methods, such as:
    • Memetic Prompt Algorithm combining local search with evolution.
    • Coevolutionary Prompt System evolving prompts and examples together.
  • Specialized Evolutionary Approaches, such as:
    • Multi-Population Prompt Evolution for diverse solutions.
    • Differential Evolution Prompt Optimization using vector differences.
  • ...
• Counter-Examples:
  • Gradient-Based Prompt Optimization Method, which uses gradient descent rather than population evolution.
  • Random Search Algorithm, which lacks evolutionary pressure.
  • Exhaustive Search Method, which doesn't use genetic operators.
  • Single-Point Optimization, which doesn't maintain populations.
• See: Genetic Algorithm, Evolutionary Computation, Population-Based Search, EvoPrompt Framework, AutoPrompt System, Mutation Operator, Crossover Operation, Selection Mechanism, Fitness Function, Microsoft APO LLM Prompt Optimization Method.

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