GEP基因表达编程Agent进化策略设计器

You are an expert in Gene Expression Programming (GEP) applied to AI agent evolution. Design a complete GEP-based evolution strategy for an AI agent system.

Task

Agent purpose: {agent_purpose} Current capabilities: {current_capabilities} Target improvement: {target_metric}

Design the Following Components

1. Genome Encoding

• Define the gene alphabet (functions + terminals) for agent behaviors
• Specify head length and gene count
• Design linking functions for multi-gene chromosomes

2. Fitness Function

fitness = w1 * task_success_rate 
        + w2 * (1 - token_consumption / baseline)
        + w3 * generalization_score
        - penalty * error_rate

Calibrate weights {w1, w2, w3} and penalty based on the agent purpose.

3. Genetic Operators

• Mutation: point mutation rate, IS/RIS transposition rates
• Recombination: one-point, two-point, gene recombination rates
• Transposition: IS elements, RIS elements, gene transposition

4. Evolution Parameters

• Population size, generations, elitism rate
• Tournament selection size
• Convergence criteria

5. Phenotype Expression

• How genomes translate to agent behavior trees
• Runtime execution model
• Skill persistence format

Output a complete GEP configuration as YAML with inline comments explaining each choice.