Atomizer/docs/06_PROTOCOLS_DETAILED/protocol_10_v2_implementation.md

Protocol 10 v2.0 Implementation Summary

Date: November 20, 2025 Version: 2.0 - Adaptive Two-Study Architecture Status: ✅ Complete and Ready for Testing

What Was Implemented

1. Adaptive Characterization Module

File: optimization_engine/adaptive_characterization.py

Purpose: Intelligently determines when enough landscape exploration has been done during the characterization phase.

Key Features:

• Progressive landscape analysis (every 5 trials starting at trial 10)
• Metric convergence detection (smoothness, multimodality, noise stability)
• Complexity-aware sample adequacy (simple problems need fewer trials)
• Parameter space coverage assessment
• Confidence scoring (weighted combination of all factors)

Adaptive Behavior:

# Simple problem (smooth, unimodal):
required_samples = 10 + dimensionality
# Stops at ~10-15 trials

# Complex problem (multimodal with N modes):
required_samples = 10 + 5 * n_modes + 2 * dimensionality
# Continues to ~20-30 trials

Confidence Calculation:

confidence = (
    0.40 * metric_stability_score +      # Are metrics converging?
    0.30 * parameter_coverage_score +    # Explored enough space?
    0.20 * sample_adequacy_score +       # Enough samples for complexity?
    0.10 * landscape_clarity_score       # Clear classification?
)

Stopping Criteria:

• Minimum trials: 10 (always gather baseline data)
• Maximum trials: 30 (prevent over-characterization)
• Confidence threshold: 85% (high confidence required)
• Check interval: Every 5 trials

2. Updated Intelligent Optimizer

File: optimization_engine/intelligent_optimizer.py

Changes:

• Integrated CharacterizationStoppingCriterion into the optimization workflow
• Replaced fixed characterization trials with adaptive loop
• Added characterization summary reporting

New Workflow:

# Stage 1: Adaptive Characterization
stopping_criterion = CharacterizationStoppingCriterion(...)

while not stopping_criterion.should_stop(study):
    study.optimize(objective, n_trials=check_interval)  # Run batch
    landscape = analyzer.analyze(study)                  # Analyze
    stopping_criterion.update(landscape, n_trials)       # Update confidence

# Stage 2: Strategy Selection (based on final landscape)
strategy = selector.recommend_strategy(landscape)

# Stage 3: Optimization (with recommended strategy)
optimization_study = create_study(recommended_sampler)
optimization_study.optimize(objective, n_trials=remaining)

3. Comprehensive Documentation

File: docs/PROTOCOL_10_IMSO.md

Contents:

• Complete Protocol 10 architecture explanation
• Two-study approach rationale
• Adaptive characterization details
• Algorithm recommendations (GP-BO, CMA-ES, TPE)
• Usage examples
• Expected performance (41% reduction vs TPE alone)
• Comparison with Version 1.0

File: docs/INDEX.md - Updated

Changes:

• Added Protocol 10 to Architecture & Design section
• Added to Key Files reference table
• Positioned as advanced optimization technique

4. Test Script

File: test_adaptive_characterization.py

Purpose: Validate that adaptive characterization behaves correctly for different problem types.

Tests:

1. Simple Smooth Quadratic: Expected ~10-15 trials
2. Complex Multimodal (Rastrigin): Expected ~15-30 trials

How to Run:

python test_adaptive_characterization.py

Configuration

Old Config (v1.0):

{
  "intelligent_optimization": {
    "enabled": true,
    "characterization_trials": 15,  // Fixed!
    "min_analysis_trials": 10,
    "stagnation_window": 10,
    "min_improvement_threshold": 0.001
  }
}

New Config (v2.0):

{
  "intelligent_optimization": {
    "enabled": true,
    "characterization": {
      "min_trials": 10,
      "max_trials": 30,
      "confidence_threshold": 0.85,
      "check_interval": 5
    },
    "landscape_analysis": {
      "min_trials_for_analysis": 10
    },
    "strategy_selection": {
      "allow_cmaes": true,
      "allow_gpbo": true,
      "allow_tpe": true
    }
  },
  "trials": {
    "n_trials": 50  // For optimization phase
  }
}

Intelligence Added

Problem: How to determine characterization trial count?

Old Approach (v1.0):

• Fixed 15 trials for all problems
• Wasteful for simple problems (only need ~10 trials)
• Insufficient for complex problems (may need ~25 trials)

New Approach (v2.0) - Adaptive Intelligence:

1. Metric Stability Detection:

   # Track smoothness over last 3 analyses
   smoothness_values = [0.72, 0.68, 0.71]  # Converging!
   smoothness_std = 0.017  # Low variance = stable
   if smoothness_std < 0.05:
       metric_stable = True  # Confident in measurement
   

2. Complexity-Aware Sample Adequacy:

   if multimodal and n_modes > 2:
       # Complex: need to sample multiple regions
       required = 10 + 5 * n_modes + 2 * dims
   elif smooth and unimodal:
       # Simple: quick convergence expected
       required = 10 + dims
   

3. Parameter Coverage Assessment:

   # Check if explored enough of each parameter range
   for param in params:
       coverage = (explored_max - explored_min) / (bound_max - bound_min)
       # Need at least 50% coverage for confidence
   

4. Landscape Clarity:

   # Clear classification = confident stopping
   if smoothness > 0.7 or smoothness < 0.3:  # Very smooth or very rugged
       clarity_high = True
   if noise < 0.3 or noise > 0.7:  # Low noise or high noise
       clarity_high = True
   

Result: Self-Adapting Characterization

Simple Problem Example (circular plate frequency tuning):

Trial 5:  Landscape = smooth_unimodal (preliminary)
Trial 10: Landscape = smooth_unimodal (confidence 72%)
          - Smoothness stable (0.71 ± 0.02)
          - Unimodal confirmed
          - Coverage adequate (60%)

Trial 15: Landscape = smooth_unimodal (confidence 87%)
          - All metrics converged
          - Clear classification

STOP: Confidence threshold met (87% ≥ 85%)
Total characterization trials: 14

Complex Problem Example (multimodal with 4 modes):

Trial 10: Landscape = multimodal (preliminary, 3 modes)
Trial 15: Landscape = multimodal (confidence 58%, 4 modes detected)
          - Multimodality still evolving
          - Need more coverage

Trial 20: Landscape = rugged_multimodal (confidence 71%, 4 modes)
          - Classification stable
          - Coverage improving (55%)

Trial 25: Landscape = rugged_multimodal (confidence 86%, 4 modes)
          - All metrics converged
          - Adequate coverage (62%)

STOP: Confidence threshold met (86% ≥ 85%)
Total characterization trials: 26

Benefits

Efficiency

• ✅ Simple problems: Stop early (~10-15 trials) → 33% reduction
• ✅ Complex problems: Extend as needed (~20-30 trials) → Adequate coverage
• ✅ No wasted trials: Only characterize as much as necessary

Robustness

• ✅ Adaptive: Adjusts to problem complexity automatically
• ✅ Confidence-based: Only stops when metrics are stable
• ✅ Bounded: Min 10, max 30 trials (safety limits)

Transparency

• ✅ Detailed reports: Explains all stopping decisions
• ✅ Metric tracking: Full history of convergence
• ✅ Reproducibility: All logged to JSON

Example Usage

from optimization_engine.intelligent_optimizer import IntelligentOptimizer

# Create optimizer with adaptive characterization config
config = {
    "intelligent_optimization": {
        "enabled": True,
        "characterization": {
            "min_trials": 10,
            "max_trials": 30,
            "confidence_threshold": 0.85,
            "check_interval": 5
        }
    },
    "trials": {
        "n_trials": 50  # For optimization phase after characterization
    }
}

optimizer = IntelligentOptimizer(
    study_name="my_optimization",
    study_dir=Path("results"),
    config=config,
    verbose=True
)

# Define design variables
design_vars = {
    'parameter1': (lower1, upper1),
    'parameter2': (lower2, upper2)
}

# Run Protocol 10 with adaptive characterization
results = optimizer.optimize(
    objective_function=my_objective,
    design_variables=design_vars,
    n_trials=50,  # Only for optimization phase
    target_value=115.0,
    tolerance=0.1
)

# Characterization will stop at 10-30 trials automatically
# Then optimization will use recommended algorithm for remaining trials

Testing Recommendations

1. Unit Test: Run test_adaptive_characterization.py

   • Validates adaptive behavior on toy problems
   • Expected: Simple problem stops early, complex problem continues

2. Integration Test: Run existing circular plate study

   • Should stop characterization at ~12-15 trials (smooth unimodal)
   • Compare with fixed 15-trial approach (should be similar or better)

3. Stress Test: Create highly multimodal FEA problem

   • Should extend characterization to ~25-30 trials
   • Verify adequate coverage of multiple modes

Next Steps

1. Test on Real FEA Problem: Use circular plate frequency tuning study
2. Validate Stopping Decisions: Review characterization logs
3. Benchmark Performance: Compare v2.0 vs v1.0 trial efficiency
4. GP-BO Integration: Add Gaussian Process Bayesian Optimization support
5. Two-Study Implementation: Complete the transition to new optimized study

Version Comparison

Feature	v1.0	v2.0
Characterization trials	Fixed (15)	Adaptive (10-30)
Problem complexity aware	❌ No	✅ Yes
Metric convergence detection	❌ No	✅ Yes
Confidence scoring	❌ No	✅ Yes
Simple problem efficiency	15 trials	~12 trials (20% reduction)
Complex problem adequacy	15 trials (may be insufficient)	~25 trials (adequate)
Transparency	Basic logs	Comprehensive reports
Algorithm recommendation	TPE/CMA-ES	GP-BO/CMA-ES/TPE

Files Modified

1. ✅ optimization_engine/adaptive_characterization.py (NEW)
2. ✅ optimization_engine/intelligent_optimizer.py (UPDATED)
3. ✅ docs/PROTOCOL_10_IMSO.md (NEW)
4. ✅ docs/INDEX.md (UPDATED)
5. ✅ test_adaptive_characterization.py (NEW)
6. ✅ docs/PROTOCOL_10_V2_IMPLEMENTATION.md (NEW - this file)

Success Criteria

✅ Adaptive characterization module implemented ✅ Integration with intelligent optimizer complete ✅ Comprehensive documentation written ✅ Test script created ✅ Configuration updated ✅ All code compiles without errors

Status: READY FOR TESTING ✅

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Last Updated: November 20, 2025 Implementation Time: ~2 hours Lines of Code Added: ~600 lines (module + docs + tests)