reevo

name	reevo
description	Reflective Evolution (ReEvo) method skill. USE WHEN the user explicitly requests ReEvo / Reflective Evolution, or wants evolution with reflection mechanisms that summarize failure lessons and inject them into the next generation.
triggers	["reevo","reflective evolution","reflection-based evolution"]

Reflective Evolution (ReEvo) Skill

Paper: Ye et al., "ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution", NeurIPS 2024.

1. Method Essence

ReEvo adds a reflection layer to the evolution loop (sample → evaluate → select): before each generation, it summarizes success/failure patterns from historical samples and injects the reflection into the prompt, so the next mutation "stands on experience" rather than trying blindly. Reflection has two levels:

• Long-term reflection: Overall summary of all historical individuals — "what structures work/fail, what patterns repeatedly fail"
• Short-term reflection: Local review of the most recent N individuals (N=5) — "what changed last round, how did it perform"

2. Recommended Parameters

See params.yaml in this directory for the recommended parameter configuration.

Parameter Guidance:

• population_size: 8-12 gives more material for reflection; start with 8.
• mutation_rate: 0.5 is balanced; increase for more diversity, decrease for stability.
• max_sample_nums: ReEvo is more sample-efficient than EoH; 100 is often sufficient.

What Happens During Evolution

1. Population initialized; reflection buffer empty
2. Each generation:
   • Compute short-term reflection (last 5 individuals) and long-term reflection (all history)
   • Generate offspring via LLM with reflection-augmented prompts
   • Evaluate, select, update population
3. Reflection evolves: as more individuals are evaluated, reflections become more insightful
4. Common pattern: early generations explore broadly; later generations refine based on accumulated wisdom

Common Pitfalls

• Reflection becomes too vague → reduce window size or add more specific prompts
• No improvement after reflection → check if the problem is too constrained for the operator
• Overfitting to reflection → occasionally force exploration with fresh random mutations

4. Acceptance Criteria

• Reflection prompts appear in the evolution log before each generation
• Population shows improvement correlated with reflection insights
• Short-term and long-term reflections are distinct (not redundant)
• Best individual incorporates ideas from reflection (traceable in code changes)