torsh-backend 0.1.2

//! Parameter Management and Tuning Module
//!
//! This module provides comprehensive parameter management and automated tuning capabilities
//! for CUDA memory optimization, including Bayesian optimization, hyperparameter search,
//! multi-fidelity optimization, and advanced parameter space exploration.

use scirs2_core::ndarray::Array2;
use std::collections::HashMap;
use std::sync::{Arc, RwLock};
use std::time::{Duration, Instant};

// ============================================================================
// Stub implementations for missing types
// ============================================================================

/// Dependency condition type (stub implementation)
#[derive(Debug, Clone)]
pub enum DependencyCondition {
    ValueEquals(f64),
    ValueInRange { min: f64, max: f64 },
}

/// Convergence analysis result (stub implementation)
#[derive(Debug, Clone, Default)]
pub struct ConvergenceAnalysis {}

/// Parameter space representation (stub implementation)
#[derive(Debug)]
pub struct ParameterSpace {}

impl ParameterSpace {
    fn new() -> Self {
        Self {}
    }
}

/// Parameter selection strategy (stub implementation)
#[derive(Debug, Clone)]
pub enum ParameterSelectionStrategy {
    All,
    ByType(ParameterType),
    ByCategory(String),
    Explicit(Vec<String>),
    HighSensitivity,
}

/// Space analysis result (stub implementation)
#[derive(Debug, Clone, Default)]
pub struct SpaceAnalysis {}

/// Parameter state (stub implementation)
#[derive(Debug, Clone, Default)]
pub struct ParameterState {}

/// Optimization history summary (stub implementation)
#[derive(Debug, Clone, Default)]
pub struct OptimizationHistorySummary {}

/// Tuning step result (stub implementation)
#[derive(Debug, Clone, Default)]
pub struct TuningStepResult {}

/// Bayesian optimization result (stub implementation)
#[derive(Debug, Clone, Default)]
pub struct BayesianOptimizationResult {}

/// Space exploration result (stub implementation)
#[derive(Debug, Clone, Default)]
pub struct SpaceExplorationResult {}

/// Parameter analytics dashboard (stub implementation)
#[derive(Debug, Clone, Default)]
pub struct ParameterAnalyticsDashboard {}

/// Auto tuning session config (stub implementation)
#[derive(Debug, Clone, Default)]
pub struct AutoTuningSessionConfig {}

/// Resource usage (stub implementation)
#[derive(Debug, Clone, Default)]
pub struct ResourceUsage {}

/// Parameter registry statistics (stub implementation)
#[derive(Debug, Clone, Default)]
pub struct ParameterRegistryStatistics {}

// Additional stub types for parameters module
#[derive(Debug, Clone, PartialEq, Eq, Hash, Default)]
pub enum ParameterCategory {
    #[default]
    General,
    Memory,
    Performance,
    Algorithm,
    Numerical,
    Hardware,
    Scheduling,
    Custom(String),
}

#[derive(Debug, Clone, Default)]
pub struct ParameterDependency {}
#[derive(Debug, Clone, Default)]
pub struct ParameterValidationRule {}
#[derive(Debug, Clone, Default)]
pub struct AutoTuningConfig {
    pub enabled: bool,
}
#[derive(Debug, Clone, Default)]
pub struct SearchSpace {}
#[derive(Debug, Clone, Default)]
pub struct OptimizationRecord {}
#[derive(Debug, Clone, Default)]
pub struct ParameterQualityMetrics {}
#[derive(Debug, Clone, Default)]
pub struct StabilityAnalysis {}
#[derive(Debug, Clone, Default)]
pub struct CorrelationData {}
#[derive(Debug, Clone, Default)]
pub struct ParameterMetadata {}
#[derive(Debug, Clone, Default)]
pub struct ParameterLifecycle {}
#[derive(Debug, Clone, Default)]
pub struct DistributionType {}
#[derive(Debug, Clone, Default)]
pub struct FunctionType {}
#[derive(Debug, Clone, Default)]
pub struct ComplexParameterValue {}
#[derive(Debug, Clone, Default)]
pub struct DynamicParameterValue {}
#[derive(Debug, Clone, Default)]
pub struct ParameterCondition {}
#[derive(Debug, Clone, Default)]
pub struct ParameterConstraint {}
#[derive(Debug, Clone, Default)]
pub struct ParameterValidator {}
#[derive(Debug, Clone, Default)]
pub struct BoundsType {}
#[derive(Debug, Clone, Default)]
pub struct BoundsAdaptationRule {}
#[derive(Debug, Clone, Default)]
pub struct ViolationPenaltyConfig {}
#[derive(Debug, Clone, Default)]
pub struct TuningContext {}
#[derive(Debug, Clone, Default)]
pub struct ResourceCost {}
#[derive(Debug, Clone, Default)]
pub struct TuningMetadata {}
#[derive(Debug, Clone, Default)]
pub struct CrossValidationResults {}
#[derive(Debug, Clone, Default)]
pub struct StatisticalSignificance {}
#[derive(Debug, Clone, Default)]
pub struct ConvergenceInfo {}
#[derive(Debug, Clone, Default)]
pub struct ExplorationInfo {}
#[derive(Debug, Clone, Default)]
pub struct MultiObjectiveResults {}
#[derive(Debug, Clone, Default)]
pub struct UncertaintyQuantification {}
#[derive(Debug, Clone, Default)]
pub struct TuningScheduler {}
#[derive(Debug, Clone, Default)]
pub struct TuningPerformanceTracker {}
#[derive(Debug, Clone, Default)]
pub struct TuningResourceManager {}
#[derive(Debug, Clone, Default)]
pub struct EarlyStoppingSystem {}
#[derive(Debug, Clone, Default)]
pub struct MultiObjectiveTuning {}
#[derive(Debug, Clone, Default)]
pub struct DistributedTuningCoordinator {}
#[derive(Debug, Clone, Default)]
pub struct TuningResultAnalyzer {}
#[derive(Debug, Clone, Default)]
pub struct AdaptiveTuningController {}
#[derive(Debug, Clone, Default)]
pub struct TuningRecommendationEngine {}
#[derive(Debug, Clone, Default)]
pub struct BayesianOptimizationEngine {}
#[derive(Debug, Clone, Default)]
pub struct GridSearchOptimizer {}
#[derive(Debug, Clone, Default)]
pub struct RandomSearchOptimizer {}
#[derive(Debug, Clone, Default)]
pub struct EvolutionaryOptimizer {}
#[derive(Debug, Clone, Default)]
pub struct ParticleSwarmOptimizer {}
#[derive(Debug, Clone, Default)]
pub struct DifferentialEvolution {}
#[derive(Debug, Clone, Default)]
pub struct HyperbandOptimizer {}
#[derive(Debug, Clone, Default)]
pub struct PopulationBasedTraining {}
#[derive(Debug, Clone, Default)]
pub struct MultiFidelityOptimizer {}
#[derive(Debug, Clone, Default)]
pub struct NeuralArchitectureSearch {}
#[derive(Debug, Clone, Default)]
pub struct MetaLearningOptimizer {}
#[derive(Debug, Clone, Default)]
pub struct GaussianProcessModel {}
#[derive(Debug, Clone, Default)]
pub struct AcquisitionFunction {}
#[derive(Debug, Clone, Default)]
pub struct AcquisitionOptimizer {}
#[derive(Debug, Clone, Default)]
pub struct PriorDistribution {}
#[derive(Debug, Clone, Default)]
pub struct KernelFunction {}
#[derive(Debug, Clone, Default)]
pub struct HyperparameterLearning {}
#[derive(Debug, Clone, Default)]
pub struct MultiObjectiveAcquisition {}
#[derive(Debug, Clone, Default)]
pub struct ConstraintHandler {}
#[derive(Debug, Clone, Default)]
pub struct UncertaintyEstimator {}
#[derive(Debug, Clone, Default)]
pub struct ActiveLearning {}
#[derive(Debug, Clone, Default)]
pub struct ThompsonSampling {}
#[derive(Debug, Clone, Default)]
pub struct SpaceVisualization {}
#[derive(Debug, Clone, Default)]
pub struct DimensionalityReduction {}
#[derive(Debug, Clone, Default)]
pub struct SpacePartitioning {}
#[derive(Debug, Clone, Default)]
pub struct CoverageAnalyzer {}
#[derive(Debug, Clone, Default)]
pub struct SensitivityAnalyzer {}
#[derive(Debug, Clone, Default)]
pub struct FeatureImportanceAnalyzer {}
#[derive(Debug, Clone, Default)]
pub struct TopologyAnalyzer {}
#[derive(Debug, Clone, Default)]
pub struct ManifoldLearning {}
#[derive(Debug, Clone, Default)]
pub struct ClusteringAnalyzer {}
#[derive(Debug, Clone, Default)]
pub struct SpaceAnomalyDetector {}
#[derive(Debug, Clone, Default)]
pub struct TuningAlgorithmConfig {}
#[derive(Debug, Clone, Default)]
pub struct TuningRecord {}
#[derive(Debug, Clone, Default)]
pub struct ParameterExportConfig {}
#[derive(Debug, Clone, Default)]
pub struct ParameterExportData {}
#[derive(Debug, Clone, Default)]
pub struct ParameterImportData {}
#[derive(Debug, Clone, Default)]
pub struct ParameterImportResult {}

/// Array3 type alias for 3D arrays
pub type Array3<T> = scirs2_core::ndarray::Array<T, scirs2_core::ndarray::Ix3>;

// More stub types
#[derive(Debug, Clone, Default)]
pub struct BayesianOptimizationConfig {}
#[derive(Debug, Clone, Default)]
pub struct HyperparameterOptimizationConfig {}
#[derive(Debug, Clone, Default)]
pub struct HyperparameterOptimizationResult {}
#[derive(Debug, Clone, Default)]
pub struct SpaceExplorationConfig {}
#[derive(Debug, Clone, Default)]
pub struct ParameterCorrelationAnalysis {}
#[derive(Debug, Clone, Default)]
pub struct RecommendationContext {}
#[derive(Debug, Clone, Default)]
pub struct ParameterRecommendation {}
#[derive(Debug, Clone, Default)]
pub struct ValidationResult {}
#[derive(Debug, Clone, Default)]
pub struct ResourceBudget {}
#[derive(Debug, Clone, Default)]
pub struct QualityMetrics {}
#[derive(Debug, Clone, Default)]
pub struct SideEffect {}
#[derive(Debug, Clone, Default)]
pub struct TuningRecommendation {}
#[derive(Debug, Clone, Default)]
pub struct BayesianOptimizationSession {}
#[derive(Debug, Clone, Default)]
pub struct OptimizationContext {}
#[derive(Debug, Clone, Default)]
pub struct ConvergenceMetrics {}
#[derive(Debug, Clone, Default)]
pub struct AcquisitionPoint {}
#[derive(Debug, Clone, Default)]
pub struct ExplorationResults {}
#[derive(Debug, Clone, Default)]
pub struct ExplorationRecommendation {}
#[derive(Debug, Clone, Default)]
pub struct DependencyViolation {}
#[derive(Debug, Clone, Default)]
pub struct PerformanceInsight {}

// ============================================================================

/// Comprehensive parameter management and auto-tuning system
#[derive(Debug)]
pub struct ParameterManager {
    /// Parameter registry and storage
    parameter_registry: ParameterRegistry,
    /// Auto-tuning engine
    auto_tuning_engine: AutoTuningEngine,
    /// Hyperparameter optimization system
    hyperparameter_optimizer: HyperparameterOptimizer,
    /// Parameter space explorer
    space_explorer: ParameterSpaceExplorer,
    /// Bayesian optimization framework
    bayesian_optimizer: BayesianOptimizer,
    /// Multi-fidelity optimization system
    multi_fidelity_system: MultiFidelitySystem,
    /// Parameter validation framework
    validation_framework: ParameterValidationFramework,
    /// Parameter evolution tracker
    evolution_tracker: ParameterEvolutionTracker,
    /// Constraint satisfaction engine
    constraint_engine: ParameterConstraintEngine,
    /// Performance correlation analyzer
    correlation_analyzer: ParameterCorrelationAnalyzer,
    /// Meta-learning system for parameter optimization
    meta_learning_system: ParameterMetaLearningSystem,
    /// Real-time parameter adaptation
    adaptive_system: AdaptiveParameterSystem,
}

/// Registry for managing optimization parameters
#[derive(Debug)]
pub struct ParameterRegistry {
    /// All registered parameters
    parameters: Arc<RwLock<HashMap<String, OptimizationParameter>>>,
    /// Parameter groups and hierarchies
    parameter_groups: HashMap<String, ParameterGroup>,
    /// Parameter dependencies
    dependencies: ParameterDependencyGraph,
    /// Parameter templates
    templates: HashMap<String, ParameterTemplate>,
    /// Parameter configuration profiles
    configuration_profiles: HashMap<String, ConfigurationProfile>,
    /// Parameter versioning system
    versioning_system: ParameterVersioningSystem,
    /// Parameter metadata index
    metadata_index: ParameterMetadataIndex,
    /// Parameter usage statistics
    usage_statistics: ParameterUsageStatistics,
    /// Parameter export/import manager
    import_export_manager: ParameterImportExportManager,
}

/// Comprehensive optimization parameter definition
#[derive(Debug, Clone)]
pub struct OptimizationParameter {
    /// Unique parameter identifier
    pub id: String,
    /// Human-readable parameter name
    pub name: String,
    /// Parameter description and purpose
    pub description: String,
    /// Current parameter value
    pub value: ParameterValue,
    /// Parameter bounds and constraints
    pub bounds: Option<ParameterBounds>,
    /// Parameter sensitivity to performance
    pub sensitivity: f32,
    /// Parameter tuning history
    pub tuning_history: Vec<ParameterTuning>,
    /// Parameter type classification
    pub parameter_type: ParameterType,
    /// Parameter category
    pub category: ParameterCategory,
    /// Parameter importance score
    pub importance: f32,
    /// Parameter dependencies
    pub dependencies: Vec<ParameterDependency>,
    /// Parameter validation rules
    pub validation_rules: Vec<ParameterValidationRule>,
    /// Auto-tuning configuration
    pub auto_tuning_config: AutoTuningConfig,
    /// Parameter search space definition
    pub search_space: SearchSpace,
    /// Parameter optimization history
    pub optimization_history: Vec<OptimizationRecord>,
    /// Parameter quality metrics
    pub quality_metrics: ParameterQualityMetrics,
    /// Parameter stability analysis
    pub stability_analysis: StabilityAnalysis,
    /// Parameter correlation data
    pub correlation_data: CorrelationData,
    /// Parameter metadata
    pub metadata: ParameterMetadata,
    /// Parameter lifecycle information
    pub lifecycle: ParameterLifecycle,
}

/// Parameter value types with comprehensive support
#[derive(Debug, Clone, PartialEq)]
pub enum ParameterValue {
    /// Integer parameter
    Integer(i64),
    /// Floating point parameter
    Float(f64),
    /// Boolean parameter
    Boolean(bool),
    /// String parameter
    String(String),
    /// Array of parameter values
    Array(Vec<ParameterValue>),
    /// Object/dictionary parameter
    Object(HashMap<String, ParameterValue>),
    /// Range parameter with step
    Range {
        min: f64,
        max: f64,
        step: Option<f64>,
    },
    /// Enumerated parameter with choices
    Enum {
        choices: Vec<String>,
        selected: String,
    },
    /// Distribution parameter
    Distribution {
        distribution_type: DistributionType,
        parameters: HashMap<String, f64>,
    },
    /// Function parameter
    Function {
        function_type: FunctionType,
        parameters: HashMap<String, f64>,
    },
    /// Matrix parameter
    Matrix(Array2<f64>),
    /// Tensor parameter
    Tensor(Array3<f64>),
    /// Complex parameter combining multiple types
    Complex(Box<ComplexParameterValue>),
    /// Dynamic parameter that changes based on context
    Dynamic(Box<DynamicParameterValue>),
    /// Conditional parameter
    Conditional {
        condition: Box<ParameterCondition>,
        true_value: Box<ParameterValue>,
        false_value: Box<ParameterValue>,
    },
    /// Reference to another parameter
    Reference(String),
    /// Custom parameter type
    Custom { type_name: String, data: Vec<u8> },
}

/// Parameter bounds and constraints
#[derive(Debug, Clone)]
pub struct ParameterBounds {
    /// Minimum allowed value
    pub min: ParameterValue,
    /// Maximum allowed value
    pub max: ParameterValue,
    /// Suggested default value
    pub suggested: Option<ParameterValue>,
    /// Step size for discrete parameters
    pub step: Option<ParameterValue>,
    /// Constraint expressions
    pub constraints: Vec<ParameterConstraint>,
    /// Validation functions
    pub validators: Vec<ParameterValidator>,
    /// Bounds type
    pub bounds_type: BoundsType,
    /// Constraint satisfaction tolerance
    pub tolerance: f64,
    /// Bounds adaptation rules
    pub adaptation_rules: Vec<BoundsAdaptationRule>,
    /// Bounds violation penalties
    pub violation_penalties: ViolationPenaltyConfig,
}

/// Parameter tuning record with comprehensive information
#[derive(Debug, Clone)]
pub struct ParameterTuning {
    /// Tuning timestamp
    pub timestamp: Instant,
    /// Parameter value at tuning
    pub value: ParameterValue,
    /// Performance result achieved
    pub performance: f32,
    /// Tuning algorithm used
    pub algorithm: String,
    /// Tuning context and conditions
    pub context: TuningContext,
    /// Confidence in the tuning result
    pub confidence: f32,
    /// Resource cost of tuning
    pub cost: ResourceCost,
    /// Tuning metadata
    pub metadata: TuningMetadata,
    /// Cross-validation results
    pub cross_validation: CrossValidationResults,
    /// Statistical significance
    pub statistical_significance: StatisticalSignificance,
    /// Convergence information
    pub convergence_info: ConvergenceInfo,
    /// Exploration vs exploitation balance
    pub exploration_info: ExplorationInfo,
    /// Multi-objective results
    pub multi_objective_results: Option<MultiObjectiveResults>,
    /// Uncertainty quantification
    pub uncertainty: UncertaintyQuantification,
}

/// Parameter types for classification
#[derive(Debug, Clone, PartialEq, Eq, Hash)]
pub enum ParameterType {
    /// Hyperparameter for algorithm configuration
    Hyperparameter,
    /// Model parameter learned during training
    ModelParameter,
    /// System configuration parameter
    SystemParameter,
    /// Performance tuning parameter
    PerformanceParameter,
    /// Resource allocation parameter
    ResourceParameter,
    /// Environment configuration parameter
    EnvironmentParameter,
    /// User preference parameter
    UserParameter,
    /// Experimental parameter for research
    ExperimentalParameter,
    /// Meta-parameter for optimization control
    MetaParameter,
    /// Strategy selection parameter
    StrategyParameter,
    /// Constraint parameter
    ConstraintParameter,
    /// Quality control parameter
    QualityParameter,
    /// Security parameter
    SecurityParameter,
    /// Custom parameter type
    Custom(String),
}

/// Auto-tuning engine for automated parameter optimization
#[derive(Debug)]
pub struct AutoTuningEngine {
    /// Available tuning algorithms
    algorithms: HashMap<String, Box<dyn TuningAlgorithm>>,
    /// Active tuning sessions
    active_sessions: HashMap<String, TuningSession>,
    /// Tuning scheduler
    scheduler: TuningScheduler,
    /// Performance tracker
    performance_tracker: TuningPerformanceTracker,
    /// Resource manager
    resource_manager: TuningResourceManager,
    /// Early stopping system
    early_stopping: EarlyStoppingSystem,
    /// Multi-objective tuning support
    multi_objective_tuning: MultiObjectiveTuning,
    /// Distributed tuning coordinator
    distributed_coordinator: DistributedTuningCoordinator,
    /// Tuning result analyzer
    result_analyzer: TuningResultAnalyzer,
    /// Adaptive tuning controller
    adaptive_controller: AdaptiveTuningController,
    /// Tuning recommendation engine
    recommendation_engine: TuningRecommendationEngine,
}

/// Hyperparameter optimization system
#[derive(Debug)]
pub struct HyperparameterOptimizer {
    /// Bayesian optimization engine
    bayesian_engine: BayesianOptimizationEngine,
    /// Grid search implementation
    grid_search: GridSearchOptimizer,
    /// Random search implementation
    random_search: RandomSearchOptimizer,
    /// Evolutionary optimization
    evolutionary_optimizer: EvolutionaryOptimizer,
    /// Particle swarm optimization
    pso_optimizer: ParticleSwarmOptimizer,
    /// Differential evolution
    differential_evolution: DifferentialEvolution,
    /// Hyperband optimization
    hyperband: HyperbandOptimizer,
    /// Population-based training
    pbt_optimizer: PopulationBasedTraining,
    /// Multi-fidelity optimization
    multi_fidelity: MultiFidelityOptimizer,
    /// Neural architecture search
    nas_optimizer: NeuralArchitectureSearch,
    /// Meta-learning optimization
    meta_optimizer: MetaLearningOptimizer,
}

/// Bayesian optimization framework
#[derive(Debug)]
pub struct BayesianOptimizer {
    /// Gaussian process surrogate model
    gaussian_process: GaussianProcessModel,
    /// Acquisition function
    acquisition_function: AcquisitionFunction,
    /// Acquisition optimizer
    acquisition_optimizer: AcquisitionOptimizer,
    /// Prior distribution
    prior_distribution: PriorDistribution,
    /// Kernel function
    kernel_function: KernelFunction,
    /// Hyperparameter learning
    hyperparameter_learning: HyperparameterLearning,
    /// Multi-objective acquisition
    multi_objective_acquisition: MultiObjectiveAcquisition,
    /// Constraint handling
    constraint_handler: ConstraintHandler,
    /// Uncertainty estimation
    uncertainty_estimator: UncertaintyEstimator,
    /// Active learning component
    active_learning: ActiveLearning,
    /// Thompson sampling support
    thompson_sampling: ThompsonSampling,
}

/// Parameter space exploration system
#[derive(Debug)]
pub struct ParameterSpaceExplorer {
    /// Space sampling strategies
    sampling_strategies: HashMap<String, Box<dyn SamplingStrategy>>,
    /// Space visualization
    visualization: SpaceVisualization,
    /// Dimensionality reduction
    dimensionality_reduction: DimensionalityReduction,
    /// Space partitioning
    space_partitioning: SpacePartitioning,
    /// Coverage analysis
    coverage_analyzer: CoverageAnalyzer,
    /// Sensitivity analysis
    sensitivity_analyzer: SensitivityAnalyzer,
    /// Feature importance
    feature_importance: FeatureImportanceAnalyzer,
    /// Space topology analysis
    topology_analyzer: TopologyAnalyzer,
    /// Manifold learning
    manifold_learning: ManifoldLearning,
    /// Clustering analysis
    clustering_analyzer: ClusteringAnalyzer,
    /// Anomaly detection
    anomaly_detector: SpaceAnomalyDetector,
}

impl ParameterManager {
    /// Create a new parameter manager
    pub fn new(config: ParameterManagerConfig) -> Self {
        Self {
            parameter_registry: ParameterRegistry::new(config.registry_config.clone()),
            auto_tuning_engine: AutoTuningEngine::new(config.tuning_config.clone()),
            hyperparameter_optimizer: HyperparameterOptimizer::new(
                config.hyperparameter_config.clone(),
            ),
            space_explorer: ParameterSpaceExplorer::new(config.exploration_config.clone()),
            bayesian_optimizer: BayesianOptimizer::new(config.bayesian_config.clone()),
            multi_fidelity_system: MultiFidelitySystem::new(config.multi_fidelity_config.clone()),
            validation_framework: ParameterValidationFramework::new(
                config.validation_config.clone(),
            ),
            evolution_tracker: ParameterEvolutionTracker::new(config.tracking_config.clone()),
            constraint_engine: ParameterConstraintEngine::new(config.constraint_config.clone()),
            correlation_analyzer: ParameterCorrelationAnalyzer::new(
                config.correlation_config.clone(),
            ),
            meta_learning_system: ParameterMetaLearningSystem::new(
                config.meta_learning_config.clone(),
            ),
            adaptive_system: AdaptiveParameterSystem::new(config.adaptive_config.clone()),
        }
    }

    /// Register a new parameter
    pub fn register_parameter(
        &mut self,
        parameter: OptimizationParameter,
    ) -> Result<(), ParameterError> {
        // Validate parameter
        self.validation_framework.validate_parameter(&parameter)?;

        // Check dependencies
        self.check_parameter_dependencies(&parameter)?;

        // Register parameter
        self.parameter_registry.register(parameter.clone())?;

        // Initialize tracking
        self.evolution_tracker
            .initialize_parameter_tracking(&parameter.id)?;

        // Update correlation analysis
        self.correlation_analyzer.add_parameter(&parameter)?;

        // Setup auto-tuning if configured
        if parameter.auto_tuning_config.enabled {
            self.setup_auto_tuning(&parameter)?;
        }

        Ok(())
    }

    /// Get parameter by ID
    pub fn get_parameter(
        &self,
        parameter_id: &str,
    ) -> Result<OptimizationParameter, ParameterError> {
        self.parameter_registry.get_parameter(parameter_id)
    }

    /// Update parameter value
    pub fn update_parameter(
        &mut self,
        parameter_id: &str,
        new_value: ParameterValue,
    ) -> Result<(), ParameterError> {
        // Get current parameter
        let mut parameter = self.get_parameter(parameter_id)?;

        // Validate new value
        self.validation_framework
            .validate_parameter_value(&parameter, &new_value)?;

        // Check constraints
        self.constraint_engine
            .check_constraints(&parameter, &new_value)?;

        // Update parameter
        parameter.value = new_value.clone();
        self.parameter_registry
            .update_parameter(parameter.clone())?;

        // Record evolution
        self.evolution_tracker
            .record_parameter_change(parameter_id, &new_value)?;

        // Update correlations
        self.correlation_analyzer
            .update_parameter_correlation(parameter_id, &new_value)?;

        Ok(())
    }

    /// Start auto-tuning session
    pub fn start_auto_tuning(
        &mut self,
        tuning_config: AutoTuningSessionConfig,
    ) -> Result<TuningSessionId, ParameterError> {
        // Validate tuning configuration
        self.validate_tuning_config(&tuning_config)?;

        // Create tuning session
        let session_id = self.auto_tuning_engine.create_session(tuning_config)?;

        // Initialize multi-fidelity if configured
        if let Some(mf_config) = &tuning_config.multi_fidelity_config {
            self.multi_fidelity_system
                .initialize_session(&session_id, mf_config)?;
        }

        Ok(session_id)
    }

    /// Execute parameter tuning step
    pub fn execute_tuning_step(
        &mut self,
        session_id: &TuningSessionId,
    ) -> Result<TuningStepResult, ParameterError> {
        // Get session
        let session = self.auto_tuning_engine.get_session(session_id)?;

        // Select next parameter configuration to evaluate
        let next_config = self.select_next_configuration(&session)?;

        // Evaluate configuration
        let evaluation_result = self.evaluate_configuration(&next_config, &session.context)?;

        // Update tuning algorithm with results
        self.auto_tuning_engine.update_algorithm(
            &session.algorithm,
            &next_config,
            &evaluation_result,
        )?;

        // Record tuning step
        self.record_tuning_step(session_id, &next_config, &evaluation_result)?;

        // Check stopping criteria
        let should_stop = self.check_stopping_criteria(session_id)?;

        Ok(TuningStepResult {
            configuration: next_config,
            evaluation: evaluation_result,
            should_stop,
            recommendations: self.generate_step_recommendations(session_id)?,
            convergence_info: self.analyze_convergence(session_id)?,
        })
    }

    /// Perform Bayesian optimization
    pub fn bayesian_optimize(
        &mut self,
        optimization_config: BayesianOptimizationConfig,
    ) -> Result<BayesianOptimizationResult, ParameterError> {
        // Initialize Bayesian optimization
        let mut optimization_session = self
            .bayesian_optimizer
            .initialize_session(optimization_config)?;

        // Run optimization loop
        let mut iteration = 0;
        let mut best_configuration = None;
        let mut best_performance = f64::NEG_INFINITY;

        while !self.should_stop_optimization(&optimization_session, iteration)? {
            // Select next configuration using acquisition function
            let next_config = self
                .bayesian_optimizer
                .select_next_configuration(&optimization_session)?;

            // Evaluate configuration
            let performance = self
                .evaluate_configuration_performance(&next_config, &optimization_session.context)?;

            // Update Gaussian process
            self.bayesian_optimizer
                .update_model(&next_config, performance)?;

            // Track best configuration
            if performance > best_performance {
                best_performance = performance;
                best_configuration = Some(next_config.clone());
            }

            // Record iteration
            self.record_bayesian_iteration(
                &optimization_session.id,
                iteration,
                &next_config,
                performance,
            )?;

            iteration += 1;
        }

        Ok(BayesianOptimizationResult {
            best_configuration: best_configuration.ok_or(ParameterError::OptimizationFailed)?,
            best_performance,
            total_iterations: iteration,
            convergence_metrics: self.calculate_convergence_metrics(&optimization_session)?,
            final_model: self.bayesian_optimizer.get_final_model()?,
            acquisition_history: self.get_acquisition_history(&optimization_session.id)?,
        })
    }

    /// Perform hyperparameter optimization
    pub fn optimize_hyperparameters(
        &mut self,
        parameters: Vec<String>,
        optimization_config: HyperparameterOptimizationConfig,
    ) -> Result<HyperparameterOptimizationResult, ParameterError> {
        // Create parameter space
        let parameter_space = self.create_parameter_space(&parameters)?;

        // Select optimization algorithm
        let optimizer = self
            .hyperparameter_optimizer
            .select_optimizer(&optimization_config)?;

        // Run optimization
        let result = optimizer.optimize(parameter_space, optimization_config)?;

        // Apply best configuration
        if optimization_config.apply_best_configuration {
            for (param_id, value) in &result.best_configuration {
                self.update_parameter(param_id, value.clone())?;
            }
        }

        // Record optimization results
        self.record_hyperparameter_optimization(&parameters, &result)?;

        Ok(result)
    }

    /// Explore parameter space
    pub fn explore_parameter_space(
        &mut self,
        exploration_config: SpaceExplorationConfig,
    ) -> Result<SpaceExplorationResult, ParameterError> {
        // Get parameters to explore
        let parameters = self.get_parameters_for_exploration(&exploration_config)?;

        // Create parameter space
        let space = self.create_parameter_space(&parameters)?;

        // Execute exploration
        let exploration_result = self
            .space_explorer
            .explore_space(space, exploration_config)?;

        // Analyze results
        let analysis = self.analyze_exploration_results(&exploration_result)?;

        // Generate recommendations
        let recommendations = self.generate_exploration_recommendations(&analysis)?;

        Ok(SpaceExplorationResult {
            explored_configurations: exploration_result.configurations,
            performance_landscape: exploration_result.performance_data,
            space_analysis: analysis,
            recommendations,
            coverage_metrics: exploration_result.coverage_metrics,
            sensitivity_analysis: exploration_result.sensitivity_data,
        })
    }

    /// Analyze parameter correlations
    pub fn analyze_parameter_correlations(
        &self,
    ) -> Result<ParameterCorrelationAnalysis, ParameterError> {
        let all_parameters = self.parameter_registry.get_all_parameters();
        self.correlation_analyzer
            .analyze_correlations(&all_parameters)
    }

    /// Get parameter recommendations
    pub fn get_parameter_recommendations(
        &self,
        context: &RecommendationContext,
    ) -> Result<Vec<ParameterRecommendation>, ParameterError> {
        // Analyze current parameter state
        let current_state = self.analyze_current_parameter_state()?;

        // Generate recommendations based on context
        let mut recommendations = Vec::new();

        // Performance-based recommendations
        recommendations.extend(self.generate_performance_recommendations(&current_state, context)?);

        // Stability-based recommendations
        recommendations.extend(self.generate_stability_recommendations(&current_state)?);

        // Resource-based recommendations
        recommendations.extend(self.generate_resource_recommendations(&current_state, context)?);

        // Meta-learning recommendations
        recommendations.extend(
            self.meta_learning_system
                .generate_recommendations(&current_state, context)?,
        );

        // Rank and filter recommendations
        self.rank_and_filter_recommendations(recommendations, context)
    }

    /// Validate parameter configuration
    pub fn validate_configuration(
        &self,
        configuration: &HashMap<String, ParameterValue>,
    ) -> Result<ValidationResult, ParameterError> {
        let mut validation_result = ValidationResult::new();

        // Validate individual parameters
        for (param_id, value) in configuration {
            let parameter = self.get_parameter(param_id)?;
            match self
                .validation_framework
                .validate_parameter_value(&parameter, value)
            {
                Ok(()) => validation_result.valid_parameters.push(param_id.clone()),
                Err(e) => validation_result
                    .invalid_parameters
                    .push((param_id.clone(), e)),
            }
        }

        // Check parameter dependencies
        let dependency_violations = self.check_configuration_dependencies(configuration)?;
        validation_result.dependency_violations = dependency_violations;

        // Check global constraints
        let constraint_violations = self
            .constraint_engine
            .check_global_constraints(configuration)?;
        validation_result.constraint_violations = constraint_violations;

        Ok(validation_result)
    }

    /// Export parameter configuration
    pub fn export_parameters(
        &self,
        export_config: ParameterExportConfig,
    ) -> Result<ParameterExportData, ParameterError> {
        self.parameter_registry.export_parameters(export_config)
    }

    /// Import parameter configuration
    pub fn import_parameters(
        &mut self,
        import_data: ParameterImportData,
    ) -> Result<ParameterImportResult, ParameterError> {
        self.parameter_registry.import_parameters(import_data)
    }

    /// Get parameter analytics dashboard
    pub fn get_analytics_dashboard(&self) -> Result<ParameterAnalyticsDashboard, ParameterError> {
        let registry_stats = self.parameter_registry.get_statistics();
        let tuning_metrics = self.auto_tuning_engine.get_metrics();
        let correlation_analysis = self.correlation_analyzer.get_analysis_summary();
        let evolution_trends = self.evolution_tracker.get_trends();

        Ok(ParameterAnalyticsDashboard {
            registry_statistics: registry_stats,
            tuning_metrics,
            correlation_analysis,
            evolution_trends,
            space_exploration_metrics: self.space_explorer.get_metrics(),
            optimization_history: self.get_optimization_history_summary(),
            performance_insights: self.generate_performance_insights()?,
        })
    }

    // Private helper methods

    fn check_parameter_dependencies(
        &self,
        parameter: &OptimizationParameter,
    ) -> Result<(), ParameterError> {
        for dependency in &parameter.dependencies {
            self.validate_parameter_dependency(parameter, dependency)?;
        }
        Ok(())
    }

    fn validate_parameter_dependency(
        &self,
        parameter: &OptimizationParameter,
        dependency: &ParameterDependency,
    ) -> Result<(), ParameterError> {
        // Check if dependency parameter exists
        let dependency_param = self.get_parameter(&dependency.parameter_id)?;

        // Validate dependency condition
        match &dependency.condition {
            DependencyCondition::ValueEquals(expected) => {
                if dependency_param.value != *expected {
                    return Err(ParameterError::DependencyViolation(format!(
                        "Parameter {} depends on {} having value {:?}, but current value is {:?}",
                        parameter.id, dependency.parameter_id, expected, dependency_param.value
                    )));
                }
            }
            DependencyCondition::ValueInRange { min, max } => {
                if !self.is_value_in_range(&dependency_param.value, min, max) {
                    return Err(ParameterError::DependencyViolation(format!(
                        "Parameter {} depends on {} being in range [{:?}, {:?}]",
                        parameter.id, dependency.parameter_id, min, max
                    )));
                }
            }
            _ => {} // Other dependency conditions
        }

        Ok(())
    }

    fn is_value_in_range(
        &self,
        value: &ParameterValue,
        min: &ParameterValue,
        max: &ParameterValue,
    ) -> bool {
        match (value, min, max) {
            (
                ParameterValue::Float(v),
                ParameterValue::Float(min_v),
                ParameterValue::Float(max_v),
            ) => v >= min_v && v <= max_v,
            (
                ParameterValue::Integer(v),
                ParameterValue::Integer(min_v),
                ParameterValue::Integer(max_v),
            ) => v >= min_v && v <= max_v,
            _ => false, // Type mismatch or unsupported comparison
        }
    }

    fn setup_auto_tuning(
        &mut self,
        parameter: &OptimizationParameter,
    ) -> Result<(), ParameterError> {
        let tuning_session_config = AutoTuningSessionConfig {
            parameter_ids: vec![parameter.id.clone()],
            algorithm: parameter.auto_tuning_config.algorithm.clone(),
            max_iterations: parameter.auto_tuning_config.max_iterations,
            target_performance: parameter.auto_tuning_config.target_performance,
            early_stopping: parameter.auto_tuning_config.early_stopping.clone(),
            multi_fidelity_config: parameter.auto_tuning_config.multi_fidelity_config.clone(),
            resource_budget: parameter.auto_tuning_config.resource_budget.clone(),
        };

        let session_id = self
            .auto_tuning_engine
            .create_session(tuning_session_config)?;

        // Store session ID for future reference
        self.parameter_registry
            .associate_tuning_session(&parameter.id, session_id)?;

        Ok(())
    }

    fn validate_tuning_config(
        &self,
        config: &AutoTuningSessionConfig,
    ) -> Result<(), ParameterError> {
        // Validate that all parameters exist
        for param_id in &config.parameter_ids {
            self.get_parameter(param_id)?;
        }

        // Validate algorithm
        if !self
            .auto_tuning_engine
            .is_algorithm_available(&config.algorithm)
        {
            return Err(ParameterError::InvalidAlgorithm(config.algorithm.clone()));
        }

        // Validate resource budget
        if let Some(budget) = &config.resource_budget {
            self.validate_resource_budget(budget)?;
        }

        Ok(())
    }

    fn validate_resource_budget(&self, budget: &ResourceBudget) -> Result<(), ParameterError> {
        if budget.max_time.is_zero() && budget.max_evaluations == 0 && budget.max_cost == 0.0 {
            return Err(ParameterError::InvalidResourceBudget(
                "At least one budget constraint must be specified".to_string(),
            ));
        }
        Ok(())
    }

    fn select_next_configuration(
        &self,
        session: &TuningSession,
    ) -> Result<ParameterConfiguration, ParameterError> {
        // Delegate to the appropriate algorithm
        self.auto_tuning_engine.select_next_configuration(session)
    }

    fn evaluate_configuration(
        &self,
        config: &ParameterConfiguration,
        context: &TuningContext,
    ) -> Result<EvaluationResult, ParameterError> {
        // Apply configuration temporarily
        let original_values = self.apply_configuration_temporarily(config)?;

        // Evaluate performance
        let performance = self.measure_performance(context)?;

        // Restore original values
        self.restore_configuration(&original_values)?;

        Ok(EvaluationResult {
            performance,
            resource_usage: self.measure_resource_usage()?,
            evaluation_time: self.get_last_evaluation_time(),
            quality_metrics: self.calculate_quality_metrics()?,
            side_effects: self.detect_side_effects()?,
        })
    }

    fn apply_configuration_temporarily(
        &self,
        config: &ParameterConfiguration,
    ) -> Result<HashMap<String, ParameterValue>, ParameterError> {
        let mut original_values = HashMap::new();

        for (param_id, new_value) in &config.parameters {
            let current_param = self.get_parameter(param_id)?;
            original_values.insert(param_id.clone(), current_param.value.clone());

            // Apply new value (this would need mutable access in practice)
            // For now, this is a conceptual implementation
        }

        Ok(original_values)
    }

    fn restore_configuration(
        &self,
        original_values: &HashMap<String, ParameterValue>,
    ) -> Result<(), ParameterError> {
        for (param_id, original_value) in original_values {
            // Restore original value (this would need mutable access in practice)
            // For now, this is a conceptual implementation
        }
        Ok(())
    }

    fn measure_performance(&self, context: &TuningContext) -> Result<f64, ParameterError> {
        // This would integrate with the actual performance measurement system
        Ok(0.5) // Placeholder
    }

    fn measure_resource_usage(&self) -> Result<ResourceUsage, ParameterError> {
        // Measure current resource usage
        Ok(ResourceUsage {
            cpu_usage: 0.5,
            memory_usage: 1024 * 1024 * 100, // 100 MB
            gpu_usage: 0.3,
            execution_time: Duration::from_millis(100),
            energy_consumption: 50.0,
        })
    }

    fn get_last_evaluation_time(&self) -> Duration {
        Duration::from_millis(100) // Placeholder
    }

    fn calculate_quality_metrics(&self) -> Result<QualityMetrics, ParameterError> {
        Ok(QualityMetrics::default())
    }

    fn detect_side_effects(&self) -> Result<Vec<SideEffect>, ParameterError> {
        Ok(Vec::new()) // Placeholder
    }

    fn record_tuning_step(
        &mut self,
        session_id: &TuningSessionId,
        config: &ParameterConfiguration,
        result: &EvaluationResult,
    ) -> Result<(), ParameterError> {
        // Record tuning step in evolution tracker
        self.evolution_tracker
            .record_tuning_step(session_id, config, result)
    }

    fn check_stopping_criteria(
        &self,
        session_id: &TuningSessionId,
    ) -> Result<bool, ParameterError> {
        // Check various stopping criteria
        let session = self.auto_tuning_engine.get_session(session_id)?;

        // Check iteration limit
        if session.iteration_count >= session.config.max_iterations {
            return Ok(true);
        }

        // Check performance target
        if let Some(target) = session.config.target_performance {
            if session.best_performance >= target {
                return Ok(true);
            }
        }

        // Check early stopping criteria
        if let Some(early_stopping) = &session.config.early_stopping {
            if self
                .early_stopping
                .should_stop(session_id, early_stopping)?
            {
                return Ok(true);
            }
        }

        // Check resource budget
        if let Some(budget) = &session.config.resource_budget {
            if self.is_budget_exhausted(session_id, budget)? {
                return Ok(true);
            }
        }

        Ok(false)
    }

    fn is_budget_exhausted(
        &self,
        session_id: &TuningSessionId,
        budget: &ResourceBudget,
    ) -> Result<bool, ParameterError> {
        let session = self.auto_tuning_engine.get_session(session_id)?;

        // Check time budget
        if session.elapsed_time >= budget.max_time {
            return Ok(true);
        }

        // Check evaluation budget
        if session.evaluation_count >= budget.max_evaluations {
            return Ok(true);
        }

        // Check cost budget
        if session.total_cost >= budget.max_cost {
            return Ok(true);
        }

        Ok(false)
    }

    fn generate_step_recommendations(
        &self,
        session_id: &TuningSessionId,
    ) -> Result<Vec<TuningRecommendation>, ParameterError> {
        // Generate recommendations for next steps
        Ok(Vec::new()) // Placeholder
    }

    fn analyze_convergence(
        &self,
        session_id: &TuningSessionId,
    ) -> Result<ConvergenceAnalysis, ParameterError> {
        // Analyze convergence of tuning process
        Ok(ConvergenceAnalysis::default())
    }

    fn should_stop_optimization(
        &self,
        session: &BayesianOptimizationSession,
        iteration: usize,
    ) -> Result<bool, ParameterError> {
        // Check stopping criteria for Bayesian optimization
        Ok(iteration >= session.config.max_iterations)
    }

    fn evaluate_configuration_performance(
        &self,
        config: &ParameterConfiguration,
        context: &OptimizationContext,
    ) -> Result<f64, ParameterError> {
        // Evaluate configuration performance
        Ok(0.5) // Placeholder
    }

    fn record_bayesian_iteration(
        &mut self,
        session_id: &str,
        iteration: usize,
        config: &ParameterConfiguration,
        performance: f64,
    ) -> Result<(), ParameterError> {
        // Record Bayesian optimization iteration
        Ok(())
    }

    fn calculate_convergence_metrics(
        &self,
        session: &BayesianOptimizationSession,
    ) -> Result<ConvergenceMetrics, ParameterError> {
        // Calculate convergence metrics
        Ok(ConvergenceMetrics::default())
    }

    fn get_acquisition_history(
        &self,
        session_id: &str,
    ) -> Result<Vec<AcquisitionPoint>, ParameterError> {
        // Get acquisition function history
        Ok(Vec::new())
    }

    fn create_parameter_space(
        &self,
        parameter_ids: &[String],
    ) -> Result<ParameterSpace, ParameterError> {
        let mut parameter_space = ParameterSpace::new();

        for param_id in parameter_ids {
            let parameter = self.get_parameter(param_id)?;
            parameter_space.add_parameter(parameter)?;
        }

        Ok(parameter_space)
    }

    fn get_parameters_for_exploration(
        &self,
        config: &SpaceExplorationConfig,
    ) -> Result<Vec<String>, ParameterError> {
        // Get parameters to explore based on configuration
        match &config.parameter_selection {
            ParameterSelectionStrategy::All => Ok(self.parameter_registry.get_all_parameter_ids()),
            ParameterSelectionStrategy::ByType(param_type) => {
                Ok(self.parameter_registry.get_parameters_by_type(param_type))
            }
            ParameterSelectionStrategy::ByCategory(category) => {
                Ok(self.parameter_registry.get_parameters_by_category(category))
            }
            ParameterSelectionStrategy::Explicit(param_ids) => Ok(param_ids.clone()),
            ParameterSelectionStrategy::HighSensitivity => {
                Ok(self.parameter_registry.get_high_sensitivity_parameters())
            }
        }
    }

    fn analyze_exploration_results(
        &self,
        results: &ExplorationResults,
    ) -> Result<SpaceAnalysis, ParameterError> {
        // Analyze space exploration results
        Ok(SpaceAnalysis::default())
    }

    fn generate_exploration_recommendations(
        &self,
        analysis: &SpaceAnalysis,
    ) -> Result<Vec<ExplorationRecommendation>, ParameterError> {
        // Generate recommendations based on exploration analysis
        Ok(Vec::new())
    }

    fn analyze_current_parameter_state(&self) -> Result<ParameterState, ParameterError> {
        // Analyze current state of all parameters
        Ok(ParameterState::default())
    }

    fn generate_performance_recommendations(
        &self,
        state: &ParameterState,
        context: &RecommendationContext,
    ) -> Result<Vec<ParameterRecommendation>, ParameterError> {
        // Generate performance-based recommendations
        Ok(Vec::new())
    }

    fn generate_stability_recommendations(
        &self,
        state: &ParameterState,
    ) -> Result<Vec<ParameterRecommendation>, ParameterError> {
        // Generate stability-based recommendations
        Ok(Vec::new())
    }

    fn generate_resource_recommendations(
        &self,
        state: &ParameterState,
        context: &RecommendationContext,
    ) -> Result<Vec<ParameterRecommendation>, ParameterError> {
        // Generate resource-based recommendations
        Ok(Vec::new())
    }

    fn rank_and_filter_recommendations(
        &self,
        recommendations: Vec<ParameterRecommendation>,
        context: &RecommendationContext,
    ) -> Result<Vec<ParameterRecommendation>, ParameterError> {
        // Rank and filter recommendations based on context
        Ok(recommendations)
    }

    fn check_configuration_dependencies(
        &self,
        configuration: &HashMap<String, ParameterValue>,
    ) -> Result<Vec<DependencyViolation>, ParameterError> {
        // Check parameter dependencies in configuration
        Ok(Vec::new())
    }

    fn record_hyperparameter_optimization(
        &mut self,
        parameters: &[String],
        result: &HyperparameterOptimizationResult,
    ) -> Result<(), ParameterError> {
        // Record hyperparameter optimization results
        Ok(())
    }

    fn get_optimization_history_summary(&self) -> OptimizationHistorySummary {
        OptimizationHistorySummary::default()
    }

    fn generate_performance_insights(&self) -> Result<Vec<PerformanceInsight>, ParameterError> {
        // Generate performance insights from parameter data
        Ok(Vec::new())
    }
}

impl ParameterRegistry {
    /// Create a new parameter registry
    pub fn new(config: ParameterRegistryConfig) -> Self {
        Self {
            parameters: Arc::new(RwLock::new(HashMap::new())),
            parameter_groups: HashMap::new(),
            dependencies: ParameterDependencyGraph::new(),
            templates: HashMap::new(),
            configuration_profiles: HashMap::new(),
            versioning_system: ParameterVersioningSystem::new(),
            metadata_index: ParameterMetadataIndex::new(),
            usage_statistics: ParameterUsageStatistics::new(),
            import_export_manager: ParameterImportExportManager::new(),
        }
    }

    /// Register a parameter
    pub fn register(&mut self, parameter: OptimizationParameter) -> Result<(), ParameterError> {
        let mut parameters = self
            .parameters
            .write()
            .map_err(|_| ParameterError::LockError)?;

        if parameters.contains_key(&parameter.id) {
            return Err(ParameterError::ParameterAlreadyExists(parameter.id));
        }

        // Update metadata index
        self.metadata_index.index_parameter(&parameter);

        // Update usage statistics
        self.usage_statistics.register_parameter(&parameter.id);

        // Store parameter
        parameters.insert(parameter.id.clone(), parameter);

        Ok(())
    }

    /// Get parameter by ID
    pub fn get_parameter(
        &self,
        parameter_id: &str,
    ) -> Result<OptimizationParameter, ParameterError> {
        let parameters = self
            .parameters
            .read()
            .map_err(|_| ParameterError::LockError)?;
        parameters
            .get(parameter_id)
            .cloned()
            .ok_or_else(|| ParameterError::ParameterNotFound(parameter_id.to_string()))
    }

    /// Update parameter
    pub fn update_parameter(
        &mut self,
        parameter: OptimizationParameter,
    ) -> Result<(), ParameterError> {
        let mut parameters = self
            .parameters
            .write()
            .map_err(|_| ParameterError::LockError)?;

        if !parameters.contains_key(&parameter.id) {
            return Err(ParameterError::ParameterNotFound(parameter.id));
        }

        parameters.insert(parameter.id.clone(), parameter);
        Ok(())
    }

    /// Get all parameters
    pub fn get_all_parameters(&self) -> Vec<OptimizationParameter> {
        let parameters = self.parameters.read().expect("lock should not be poisoned");
        parameters.values().cloned().collect()
    }

    /// Get all parameter IDs
    pub fn get_all_parameter_ids(&self) -> Vec<String> {
        let parameters = self.parameters.read().expect("lock should not be poisoned");
        parameters.keys().cloned().collect()
    }

    /// Get parameters by type
    pub fn get_parameters_by_type(&self, param_type: &ParameterType) -> Vec<String> {
        let parameters = self.parameters.read().expect("lock should not be poisoned");
        parameters
            .values()
            .filter(|p| p.parameter_type == *param_type)
            .map(|p| p.id.clone())
            .collect()
    }

    /// Get parameters by category
    pub fn get_parameters_by_category(&self, category: &ParameterCategory) -> Vec<String> {
        let parameters = self.parameters.read().expect("lock should not be poisoned");
        parameters
            .values()
            .filter(|p| p.category == *category)
            .map(|p| p.id.clone())
            .collect()
    }

    /// Get high sensitivity parameters
    pub fn get_high_sensitivity_parameters(&self) -> Vec<String> {
        let parameters = self.parameters.read().expect("lock should not be poisoned");
        parameters
            .values()
            .filter(|p| p.sensitivity > 0.7) // Threshold for high sensitivity
            .map(|p| p.id.clone())
            .collect()
    }

    /// Associate tuning session with parameter
    pub fn associate_tuning_session(
        &mut self,
        parameter_id: &str,
        session_id: TuningSessionId,
    ) -> Result<(), ParameterError> {
        // Associate tuning session with parameter for tracking
        Ok(())
    }

    /// Export parameters
    pub fn export_parameters(
        &self,
        config: ParameterExportConfig,
    ) -> Result<ParameterExportData, ParameterError> {
        self.import_export_manager
            .export_parameters(&self.parameters, config)
    }

    /// Import parameters
    pub fn import_parameters(
        &mut self,
        import_data: ParameterImportData,
    ) -> Result<ParameterImportResult, ParameterError> {
        self.import_export_manager
            .import_parameters(&mut self.parameters, import_data)
    }

    /// Get registry statistics
    pub fn get_statistics(&self) -> ParameterRegistryStatistics {
        let parameters = self.parameters.read().expect("lock should not be poisoned");
        ParameterRegistryStatistics {
            total_parameters: parameters.len(),
            parameters_by_type: self.count_parameters_by_type(&parameters),
            parameters_by_category: self.count_parameters_by_category(&parameters),
            high_sensitivity_count: parameters.values().filter(|p| p.sensitivity > 0.7).count(),
            auto_tuning_enabled_count: parameters
                .values()
                .filter(|p| p.auto_tuning_config.enabled)
                .count(),
        }
    }

    // Private helper methods

    fn count_parameters_by_type(
        &self,
        parameters: &HashMap<String, OptimizationParameter>,
    ) -> HashMap<ParameterType, usize> {
        let mut counts = HashMap::new();
        for param in parameters.values() {
            *counts.entry(param.parameter_type).or_insert(0) += 1;
        }
        counts
    }

    fn count_parameters_by_category(
        &self,
        parameters: &HashMap<String, OptimizationParameter>,
    ) -> HashMap<ParameterCategory, usize> {
        let mut counts = HashMap::new();
        for param in parameters.values() {
            *counts.entry(param.category).or_insert(0) += 1;
        }
        counts
    }
}

// Error handling
#[derive(Debug)]
pub enum ParameterError {
    ParameterNotFound(String),
    ParameterAlreadyExists(String),
    InvalidParameterValue(String),
    ValidationFailed(String),
    DependencyViolation(String),
    ConstraintViolation(String),
    OptimizationFailed,
    InvalidAlgorithm(String),
    InvalidResourceBudget(String),
    TuningFailed(String),
    LockError,
    ConfigurationError(String),
    ImportExportError(String),
    SessionNotFound(String),
    InsufficientData,
    AlgorithmError(String),
    ResourceExhausted,
}

impl std::fmt::Display for ParameterError {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        match self {
            ParameterError::ParameterNotFound(id) => write!(f, "Parameter not found: {}", id),
            ParameterError::ParameterAlreadyExists(id) => {
                write!(f, "Parameter already exists: {}", id)
            }
            ParameterError::InvalidParameterValue(msg) => {
                write!(f, "Invalid parameter value: {}", msg)
            }
            ParameterError::ValidationFailed(msg) => write!(f, "Validation failed: {}", msg),
            ParameterError::DependencyViolation(msg) => write!(f, "Dependency violation: {}", msg),
            ParameterError::ConstraintViolation(msg) => write!(f, "Constraint violation: {}", msg),
            ParameterError::OptimizationFailed => write!(f, "Optimization failed"),
            ParameterError::InvalidAlgorithm(alg) => write!(f, "Invalid algorithm: {}", alg),
            ParameterError::InvalidResourceBudget(msg) => {
                write!(f, "Invalid resource budget: {}", msg)
            }
            ParameterError::TuningFailed(msg) => write!(f, "Tuning failed: {}", msg),
            ParameterError::LockError => write!(f, "Failed to acquire lock"),
            ParameterError::ConfigurationError(msg) => write!(f, "Configuration error: {}", msg),
            ParameterError::ImportExportError(msg) => write!(f, "Import/export error: {}", msg),
            ParameterError::SessionNotFound(id) => write!(f, "Tuning session not found: {}", id),
            ParameterError::InsufficientData => write!(f, "Insufficient data for optimization"),
            ParameterError::AlgorithmError(msg) => write!(f, "Algorithm error: {}", msg),
            ParameterError::ResourceExhausted => write!(f, "Resource budget exhausted"),
        }
    }
}

impl std::error::Error for ParameterError {}

// Supporting trait definitions and implementations
pub trait TuningAlgorithm: std::fmt::Debug + Send + Sync {
    fn initialize(&mut self, config: &TuningAlgorithmConfig) -> Result<(), ParameterError>;
    fn suggest_next(
        &self,
        history: &[TuningRecord],
    ) -> Result<ParameterConfiguration, ParameterError>;
    fn update(
        &mut self,
        config: &ParameterConfiguration,
        result: &EvaluationResult,
    ) -> Result<(), ParameterError>;
    fn get_name(&self) -> &str;
    fn is_converged(&self) -> bool;
}

pub trait SamplingStrategy: std::fmt::Debug + Send + Sync {
    fn sample(
        &self,
        space: &ParameterSpace,
        num_samples: usize,
    ) -> Result<Vec<ParameterConfiguration>, ParameterError>;
    fn get_name(&self) -> &str;
    fn get_parameters(&self) -> HashMap<String, f64>;
}

// Placeholder implementations for supporting structures
// (Due to space constraints, providing abbreviated versions)

#[derive(Debug, Default)]
pub struct ParameterManagerConfig;
#[derive(Debug, Default)]
pub struct ParameterRegistryConfig;
#[derive(Debug, Default)]
pub struct MultiFidelitySystem;
#[derive(Debug, Default)]
pub struct ParameterValidationFramework;
#[derive(Debug, Default)]
pub struct ParameterEvolutionTracker;
#[derive(Debug, Default)]
pub struct ParameterConstraintEngine;
#[derive(Debug, Default)]
pub struct ParameterCorrelationAnalyzer;
#[derive(Debug, Default)]
pub struct ParameterMetaLearningSystem;
#[derive(Debug, Default)]
pub struct AdaptiveParameterSystem;

// Additional supporting structures (abbreviated for space)
#[derive(Debug, Default)]
pub struct ParameterGroup;
#[derive(Debug, Default)]
pub struct ParameterDependencyGraph;
#[derive(Debug, Default)]
pub struct ParameterTemplate;
#[derive(Debug, Default)]
pub struct ConfigurationProfile;
#[derive(Debug, Default)]
pub struct ParameterVersioningSystem;
#[derive(Debug, Default)]
pub struct ParameterMetadataIndex;
#[derive(Debug, Default)]
pub struct ParameterUsageStatistics;
#[derive(Debug, Default)]
pub struct ParameterImportExportManager;

// Many more supporting structures would be implemented for complete functionality
// This represents the core architecture and main interfaces

impl AutoTuningEngine {
    fn new(config: AutoTuningEngineConfig) -> Self {
        Self
    }
    fn create_session(
        &mut self,
        config: AutoTuningSessionConfig,
    ) -> Result<TuningSessionId, ParameterError> {
        Ok("session_1".to_string())
    }
    fn get_session(&self, id: &TuningSessionId) -> Result<TuningSession, ParameterError> {
        Ok(TuningSession::default())
    }
    fn is_algorithm_available(&self, algorithm: &str) -> bool {
        true
    }
    fn select_next_configuration(
        &self,
        session: &TuningSession,
    ) -> Result<ParameterConfiguration, ParameterError> {
        Ok(ParameterConfiguration::default())
    }
    fn update_algorithm(
        &mut self,
        algorithm: &str,
        config: &ParameterConfiguration,
        result: &EvaluationResult,
    ) -> Result<(), ParameterError> {
        Ok(())
    }
    fn get_metrics(&self) -> TuningEngineMetrics {
        TuningEngineMetrics::default()
    }
}

impl ParameterValidationFramework {
    fn new(config: ParameterValidationConfig) -> Self {
        Self
    }
    fn validate_parameter(&self, parameter: &OptimizationParameter) -> Result<(), ParameterError> {
        Ok(())
    }
    fn validate_parameter_value(
        &self,
        parameter: &OptimizationParameter,
        value: &ParameterValue,
    ) -> Result<(), ParameterError> {
        Ok(())
    }
}

// Type aliases and additional structures
pub type TuningSessionId = String;

#[derive(Debug, Default)]
pub struct AutoTuningEngineConfig;
#[derive(Debug, Default)]
pub struct ParameterValidationConfig;
#[derive(Debug, Default)]
pub struct TuningSession;
#[derive(Debug, Default)]
pub struct ParameterConfiguration;
#[derive(Debug, Default)]
pub struct EvaluationResult;
#[derive(Debug, Default)]
pub struct TuningEngineMetrics;

// This represents the comprehensive parameter management module architecture