mcts-ahd

name	mcts-ahd
description	Monte Carlo Tree Search for Automatic Heuristic Design (MCTS-AHD) method skill. USE WHEN the user explicitly requests MCTS / Monte Carlo Tree Search for heuristic design, or wants tree-structured search with UCT-based exploration-exploitation balance.
triggers	["mcts","mcts-ahd","monte carlo tree search","tree search optimization"]

MCTS-AHD Skill

Paper: Zheng et al., "Monte Carlo Tree Search for Comprehensive Exploration in LLM-based Automatic Heuristic Design", ICML 2025.

1. Method Essence

MCTS-AHD models heuristic search as a tree: the root is a template/initial solution, each node is a heuristic implementation, and edges are "mutations producing child nodes". Four-step cycle:

1. Selection: From root, select a node using the UCT formula
2. Expansion: Use LLM to generate child node variants from the selected node
3. Evaluation: Evaluate the child node, obtain reward
4. Backpropagation: Update Q-value and visit count for all ancestor nodes along the path

UCT formula:

UCT = (Q - q_min) / (q_max - q_min) + λ₀ × eval_remain × √(log(parent.visits + 1) / node.visits)

• Term 1: Exploitation — normalized Q-value, favoring high-reward nodes
• Term 2: Exploration — weighted by λ₀×remaining budget, favoring less-visited nodes
• α and λ₀ jointly control the exploration-exploitation balance (reference: α=0.5, λ₀=0.1)

2. Recommended Parameters

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

Parameter Guidance:

• max_generations: Interpreted as MCTS iterations; 500-2000 typical.
• init_size: 4-6 works well for most problems.
• population_size: 10-20 for moderate problems; increase for complex landscapes.
• alpha: Higher (0.7-1.0) = wider tree; lower (0.3-0.5) = deeper tree.
• lambda_0: Higher (0.2-0.5) = more exploration; lower (0.05-0.1) = more exploitation.
• max_depth: 10-15 prevents infinite deepening.

What Happens During Evolution

1. Root node initialized from seed or template
2. Each iteration:
   • Select a node using UCT (balances Q-value and visit count)
   • Expand: generate 1-3 child nodes via LLM mutation
   • Evaluate children, update Q-values
   • Backpropagate: update all ancestors' visit counts and Q-values
3. Tree grows asymmetrically — high-value branches get explored more
4. Late iterations converge to the best branch for refinement

Common Pitfalls

• Tree too shallow → increase max_depth or decrease lambda_0
• Tree too wide → decrease alpha or increase lambda_0
• All nodes similar → increase diversity in mutation prompts
• Budget exhausted before convergence → increase max_sample_nums

4. Acceptance Criteria

• Tree structure visible in evolution log (parent-child relationships)
• UCT scores reported for each selection decision
• Depth and breadth both explored (not just one path)
• Late iterations focus on refining the best branch
• Final best individual is a leaf node with highest Q-value