feat: Major update with validators, skills, dashboard, and docs reorganization

- Add validation framework (config, model, results, study validators)
- Add Claude Code skills (create-study, run-optimization, generate-report,
  troubleshoot, analyze-model)
- Add Atomizer Dashboard (React frontend + FastAPI backend)
- Reorganize docs into structured directories (00-09)
- Add neural surrogate modules and training infrastructure
- Add multi-objective optimization support

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

examples/optimization_config_protocol10.json

@@ -0,0 +1,102 @@
+{
+  "_description": "Protocol 10: Intelligent Multi-Strategy Optimization Configuration",
+  "_version": "1.0",
+  "_date": "2025-11-19",
+
+  "study_name": "intelligent_optimization_example",
+  "direction": "minimize",
+
+  "intelligent_optimization": {
+    "_description": "Protocol 10 - Automatic landscape analysis and strategy selection",
+    "enabled": true,
+
+    "characterization_trials": 15,
+    "_comment_characterization": "Initial random exploration trials for landscape analysis (default: 15)",
+
+    "stagnation_window": 10,
+    "_comment_stagnation": "Number of trials to check for stagnation before switching (default: 10)",
+
+    "min_improvement_threshold": 0.001,
+    "_comment_threshold": "Minimum relative improvement to avoid stagnation (0.001 = 0.1%)",
+
+    "min_analysis_trials": 10,
+    "_comment_min_trials": "Minimum trials before landscape analysis is reliable (default: 10)",
+
+    "reanalysis_interval": 15,
+    "_comment_reanalysis": "Re-analyze landscape every N trials to detect changes (default: 15)",
+
+    "strategy_preferences": {
+      "_description": "Optional user preferences for strategy selection",
+      "prefer_cmaes_for_smooth": true,
+      "prefer_tpe_for_multimodal": true,
+      "enable_hybrid_strategies": false,
+      "_comment_hybrid": "GP→CMA-ES hybrid (not yet implemented)"
+    }
+  },
+
+  "sampler": {
+    "_description": "This will be overridden by Protocol 10's intelligent selection, but used as fallback",
+    "type": "TPESampler",
+    "params": {
+      "n_startup_trials": 10,
+      "n_ei_candidates": 24,
+      "multivariate": true,
+      "warn_independent_sampling": true
+    }
+  },
+
+  "pruner": {
+    "_description": "Early stopping for unpromising trials (optional)",
+    "type": "MedianPruner",
+    "params": {
+      "n_startup_trials": 5,
+      "n_warmup_steps": 0
+    }
+  },
+
+  "adaptive_strategy": {
+    "_description": "Protocol 8 - Adaptive exploitation based on surrogate confidence",
+    "enabled": true,
+    "min_confidence_for_exploitation": 0.65,
+    "min_trials_for_confidence": 15,
+    "target_confidence_metrics": {
+      "convergence_weight": 0.4,
+      "coverage_weight": 0.3,
+      "stability_weight": 0.3
+    }
+  },
+
+  "trials": {
+    "n_trials": 100,
+    "timeout": null,
+    "_comment_timeout": "Maximum time in seconds (null = no timeout)",
+    "catch": []
+  },
+
+  "reporting": {
+    "auto_generate_plots": true,
+    "include_optuna_visualizations": true,
+    "include_confidence_report": true,
+    "include_strategy_performance": true,
+    "save_intelligence_report": true,
+    "_comment_intelligence": "Saves landscape analysis and strategy decisions to JSON"
+  },
+
+  "verbosity": {
+    "print_landscape_report": true,
+    "print_strategy_recommendation": true,
+    "print_phase_transitions": true,
+    "print_confidence_updates": true,
+    "log_to_file": true
+  },
+
+  "experimental": {
+    "_description": "Experimental features (use at your own risk)",
+    "enable_transfer_learning": false,
+    "_comment_transfer": "Learn from previous studies (not yet implemented)",
+    "enable_parallel_strategies": false,
+    "_comment_parallel": "Run multiple strategies concurrently (not yet implemented)"
+  },
+
+  "optimization_notes": "This configuration enables Protocol 10: Intelligent Multi-Strategy Optimization. Atomizer will automatically characterize your problem landscape, select the best optimization algorithm, and switch strategies dynamically if needed. All decisions are logged for transparency."
+}