optirs-learned 0.3.1

// Neural Architecture Search (NAS) Module
//
// This module provides a comprehensive neural architecture search framework
// with support for multiple search strategies, evaluation methods, and encoding schemes.

#[allow(dead_code)]

pub mod search_strategies;
pub mod evaluation;
pub mod encoding;

use scirs2_core::numeric::Float;
use std::collections::HashMap;
use std::fmt::Debug;

use crate::error::{OptimError, Result};

// Re-export key types for convenience
pub use search_strategies::{
    evolutionary::{EvolutionarySearcher, EvolutionaryConfig, Individual},
    reinforcement_learning::{RLArchitectureAgent, RLNASConfig},
    bayesian_optimization::{BayesianArchitectureOptimizer, BayesianOptConfig},
    gradient_based::{DARTSSearcher, GradientBasedNASConfig},
};

pub use evaluation::{
    performance_estimators::{PerformanceEstimator, PerformanceEstimatorConfig},
    multi_objective::{MultiObjectiveEvaluator, MultiObjectiveConfig},
    hardware_aware::{HardwareAwareEvaluator, HardwareAwareConfig},
};

pub use encoding::{
    architecture_encoding::{ArchitectureEncoder, ArchitectureEncodingConfig, EncodingType},
    search_space::{SearchSpace, SearchSpaceConfig, SearchSpaceType},
    mutation_operators::{ArchitectureMutator, MutationConfig, MutationType},
};

/// Configuration for the adaptive NAS system
#[derive(Debug, Clone)]
pub struct AdaptiveNASConfig<T: Float + Debug + Send + Sync + 'static> {
    /// Search strategy configuration
    pub search_strategy: SearchStrategyConfig<T>,
    
    /// Evaluation configuration
    pub evaluation_config: EvaluationConfig<T>,
    
    /// Encoding configuration
    pub encoding_config: EncodingConfig<T>,
    
    /// Performance requirements
    pub performance_requirements: PerformanceRequirements<T>,
    
    /// Resource constraints
    pub resource_constraints: ResourceConstraints<T>,
    
    /// Search termination criteria
    pub termination_criteria: TerminationCriteria<T>,
}

/// Search strategy configuration
#[derive(Debug, Clone)]
pub struct SearchStrategyConfig<T: Float + Debug + Send + Sync + 'static> {
    /// Primary search strategy
    pub primary_strategy: SearchStrategy,
    
    /// Strategy-specific parameters
    pub strategy_params: HashMap<String, T>,
    
    /// Hybrid strategy configuration
    pub hybrid_config: Option<HybridSearchConfig<T>>,
    
    /// Search space configuration
    pub search_space_config: SearchSpaceConfig<T>,
    
    /// Population size (for population-based methods)
    pub population_size: usize,
    
    /// Number of generations/iterations
    pub max_iterations: usize,
}

/// Available search strategies
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum SearchStrategy {
    /// Evolutionary algorithm
    Evolutionary,
    
    /// Reinforcement learning
    ReinforcementLearning,
    
    /// Bayesian optimization
    BayesianOptimization,
    
    /// Gradient-based (DARTS)
    GradientBased,
    
    /// Random search
    Random,
    
    /// Grid search
    Grid,
    
    /// Hybrid approach
    Hybrid,
}

/// Hybrid search configuration
#[derive(Debug, Clone)]
pub struct HybridSearchConfig<T: Float + Debug + Send + Sync + 'static> {
    /// Strategies to combine
    pub strategies: Vec<SearchStrategy>,
    
    /// Strategy weights
    pub strategy_weights: HashMap<SearchStrategy, T>,
    
    /// Switch criteria between strategies
    pub switch_criteria: SwitchCriteria<T>,
    
    /// Synchronization method
    pub synchronization: SynchronizationMethod,
}

/// Criteria for switching between strategies
#[derive(Debug, Clone)]
pub struct SwitchCriteria<T: Float + Debug + Send + Sync + 'static> {
    /// Performance improvement threshold
    pub improvement_threshold: T,
    
    /// Iterations without improvement
    pub stagnation_limit: usize,
    
    /// Diversity threshold
    pub diversity_threshold: T,
    
    /// Resource utilization threshold
    pub resource_threshold: T,
}

/// Synchronization methods for hybrid search
#[derive(Debug, Clone, Copy)]
pub enum SynchronizationMethod {
    /// Sequential execution
    Sequential,
    
    /// Parallel execution with periodic sync
    Parallel,
    
    /// Asynchronous execution
    Asynchronous,
    
    /// Pipeline execution
    Pipeline,
}

/// Evaluation configuration
#[derive(Debug, Clone)]
pub struct EvaluationConfig<T: Float + Debug + Send + Sync + 'static> {
    /// Evaluation methods
    pub evaluation_methods: Vec<EvaluationMethod>,
    
    /// Performance estimation settings
    pub performance_estimation: PerformanceEstimatorConfig<T>,
    
    /// Multi-objective settings
    pub multi_objective: Option<MultiObjectiveConfig<T>>,
    
    /// Hardware-aware settings
    pub hardware_aware: Option<HardwareAwareConfig<T>>,
    
    /// Early stopping configuration
    pub early_stopping: EarlyStoppingConfig<T>,
}

/// Available evaluation methods
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum EvaluationMethod {
    /// Full training evaluation
    FullTraining,
    
    /// Performance estimation
    PerformanceEstimation,
    
    /// Multi-objective evaluation
    MultiObjective,
    
    /// Hardware-aware evaluation
    HardwareAware,
    
    /// Proxy evaluation
    Proxy,
}

/// Early stopping configuration
#[derive(Debug, Clone)]
pub struct EarlyStoppingConfig<T: Float + Debug + Send + Sync + 'static> {
    /// Enable early stopping
    pub enabled: bool,
    
    /// Performance improvement threshold
    pub improvement_threshold: T,
    
    /// Patience (evaluations without improvement)
    pub patience: usize,
    
    /// Minimum evaluations before early stopping
    pub min_evaluations: usize,
}

/// Encoding configuration
#[derive(Debug, Clone)]
pub struct EncodingConfig<T: Float + Debug + Send + Sync + 'static> {
    /// Architecture encoding settings
    pub architecture_encoding: ArchitectureEncodingConfig,
    
    /// Mutation settings
    pub mutation_config: MutationConfig<T>,
    
    /// Crossover settings (for evolutionary methods)
    pub crossover_config: Option<CrossoverConfig<T>>,
    
    /// Encoding validation settings
    pub validation_config: ValidationConfig,
}

/// Crossover configuration
#[derive(Debug, Clone)]
pub struct CrossoverConfig<T: Float + Debug + Send + Sync + 'static> {
    /// Crossover probability
    pub crossover_probability: T,
    
    /// Crossover methods
    pub methods: Vec<CrossoverMethod>,
    
    /// Method selection weights
    pub method_weights: HashMap<CrossoverMethod, T>,
}

/// Crossover methods
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum CrossoverMethod {
    /// Single-point crossover
    SinglePoint,
    
    /// Multi-point crossover
    MultiPoint,
    
    /// Uniform crossover
    Uniform,
    
    /// Semantic crossover
    Semantic,
}

/// Validation configuration
#[derive(Debug, Clone)]
pub struct ValidationConfig {
    /// Enable architecture validation
    pub enabled: bool,
    
    /// Validation strictness
    pub strictness: ValidationStrictness,
    
    /// Custom validation rules
    pub custom_rules: Vec<String>,
}

/// Validation strictness levels
#[derive(Debug, Clone, Copy)]
pub enum ValidationStrictness {
    Permissive, Standard, Strict, Custom,
}

/// Performance requirements
#[derive(Debug, Clone)]
pub struct PerformanceRequirements<T: Float + Debug + Send + Sync + 'static> {
    /// Minimum accuracy target
    pub min_accuracy: Option<T>,
    
    /// Maximum training time
    pub max_training_time: Option<T>,
    
    /// Maximum inference latency
    pub max_inference_latency: Option<T>,
    
    /// Maximum memory usage
    pub max_memory_usage: Option<usize>,
    
    /// Target performance metrics
    pub target_metrics: HashMap<String, T>,
}

/// Resource constraints
#[derive(Debug, Clone)]
pub struct ResourceConstraints<T: Float + Debug + Send + Sync + 'static> {
    /// Maximum computational budget
    pub max_compute_budget: Option<T>,
    
    /// Maximum wall-clock time
    pub max_wall_time: Option<u64>,
    
    /// Maximum architecture evaluations
    pub max_evaluations: Option<usize>,
    
    /// Memory limits per evaluation
    pub memory_limits: Option<usize>,
    
    /// Parallelism constraints
    pub parallelism_limits: ParallelismLimits,
}

/// Parallelism limits
#[derive(Debug, Clone)]
pub struct ParallelismLimits {
    /// Maximum parallel evaluations
    pub max_parallel_evaluations: usize,
    
    /// Maximum parallel search threads
    pub max_parallel_searches: usize,
    
    /// GPU allocation limits
    pub gpu_limits: Option<GPULimits>,
}

/// GPU resource limits
#[derive(Debug, Clone)]
pub struct GPULimits {
    /// Maximum GPUs to use
    pub max_gpus: usize,
    
    /// Memory per GPU (bytes)
    pub memory_per_gpu: usize,
    
    /// GPU utilization threshold
    pub utilization_threshold: f64,
}

/// Search termination criteria
#[derive(Debug, Clone)]
pub struct TerminationCriteria<T: Float + Debug + Send + Sync + 'static> {
    /// Maximum iterations
    pub max_iterations: Option<usize>,
    
    /// Maximum evaluations
    pub max_evaluations: Option<usize>,
    
    /// Maximum time (seconds)
    pub max_time: Option<u64>,
    
    /// Target performance threshold
    pub target_performance: Option<T>,
    
    /// Convergence criteria
    pub convergence: Option<ConvergenceCriteria<T>>,
}

/// Convergence criteria
#[derive(Debug, Clone)]
pub struct ConvergenceCriteria<T: Float + Debug + Send + Sync + 'static> {
    /// Performance improvement threshold
    pub improvement_threshold: T,
    
    /// Patience (iterations without improvement)
    pub patience: usize,
    
    /// Population diversity threshold
    pub diversity_threshold: Option<T>,
    
    /// Relative tolerance
    pub relative_tolerance: T,
}

/// Main adaptive NAS system
#[derive(Debug)]
pub struct AdaptiveNASSystem<T: Float + Debug + Send + Sync + 'static> {
    /// Configuration
    config: AdaptiveNASConfig<T>,
    
    /// Current search strategy
    current_strategy: Box<dyn NASSearchStrategy<T>>,
    
    /// Architecture evaluator
    evaluator: Box<dyn ArchitectureEvaluator<T>>,
    
    /// Architecture encoder
    encoder: ArchitectureEncoder<T>,
    
    /// Search space
    search_space: SearchSpace<T>,
    
    /// Architecture mutator
    mutator: ArchitectureMutator<T>,
    
    /// Search results
    results: SearchResults<T>,
    
    /// Search statistics
    statistics: SearchStatistics<T>,
}

/// Trait for NAS search strategies
pub trait NASSearchStrategy<T: Float + Debug + Send + Sync + 'static> {
    /// Initialize the search strategy
    fn initialize(&mut self, search_space: &SearchSpace<T>) -> Result<()>;
    
    /// Generate candidate architectures
    fn generate_candidates(&mut self) -> Result<Vec<CandidateArchitecture<T>>>;
    
    /// Update strategy with evaluation results
    fn update_with_results(&mut self, results: &[EvaluationResult<T>]) -> Result<()>;
    
    /// Check if search should terminate
    fn should_terminate(&self) -> bool;
    
    /// Get strategy-specific statistics
    fn get_statistics(&self) -> HashMap<String, f64>;
}

/// Trait for architecture evaluators
pub trait ArchitectureEvaluator<T: Float + Debug + Send + Sync + 'static> {
    /// Evaluate a single architecture
    fn evaluate(&mut self, architecture: &CandidateArchitecture<T>) -> Result<EvaluationResult<T>>;
    
    /// Batch evaluate multiple architectures
    fn batch_evaluate(&mut self, architectures: &[CandidateArchitecture<T>]) -> Result<Vec<EvaluationResult<T>>>;
    
    /// Get evaluation statistics
    fn get_evaluation_statistics(&self) -> HashMap<String, f64>;
}

/// Candidate architecture for evaluation
#[derive(Debug, Clone)]
pub struct CandidateArchitecture<T: Float + Debug + Send + Sync + 'static> {
    /// Architecture identifier
    pub id: String,
    
    /// Architecture specification
    pub specification: ArchitectureSpecification<T>,
    
    /// Encoded representation
    pub encoded: Option<encoding::architecture_encoding::EncodedArchitecture<T>>,
    
    /// Generation information
    pub generation_info: GenerationInfo,
    
    /// Metadata
    pub metadata: HashMap<String, String>,
}

/// Architecture specification
#[derive(Debug, Clone)]
pub struct ArchitectureSpecification<T: Float + Debug + Send + Sync + 'static> {
    /// Operations/layers in the architecture
    pub operations: Vec<OperationSpec<T>>,
    
    /// Connections between operations
    pub connections: Vec<ConnectionSpec<T>>,
    
    /// Global parameters
    pub global_parameters: HashMap<String, T>,
    
    /// Input/output specifications
    pub io_specification: IOSpecification,
}

/// Operation specification
#[derive(Debug, Clone)]
pub struct OperationSpec<T: Float + Debug + Send + Sync + 'static> {
    /// Operation identifier
    pub id: String,
    
    /// Operation type
    pub operation_type: String,
    
    /// Operation parameters
    pub parameters: HashMap<String, T>,
    
    /// Input shapes
    pub input_shapes: Vec<Vec<usize>>,
    
    /// Output shapes
    pub output_shapes: Vec<Vec<usize>>,
}

/// Connection specification
#[derive(Debug, Clone)]
pub struct ConnectionSpec<T: Float + Debug + Send + Sync + 'static> {
    /// Connection identifier
    pub id: String,
    
    /// Source operation
    pub source: String,
    
    /// Target operation
    pub target: String,
    
    /// Connection type
    pub connection_type: String,
    
    /// Connection parameters
    pub parameters: HashMap<String, T>,
}

/// Input/output specification
#[derive(Debug, Clone)]
pub struct IOSpecification {
    /// Input specifications
    pub inputs: Vec<TensorSpec>,
    
    /// Output specifications
    pub outputs: Vec<TensorSpec>,
}

/// Tensor specification
#[derive(Debug, Clone)]
pub struct TensorSpec {
    /// Tensor name
    pub name: String,
    
    /// Tensor shape
    pub shape: Vec<usize>,
    
    /// Data type
    pub dtype: String,
}

/// Generation information
#[derive(Debug, Clone)]
pub struct GenerationInfo {
    /// Generation method
    pub method: GenerationMethod,
    
    /// Parent architectures
    pub parents: Vec<String>,
    
    /// Generation parameters
    pub parameters: HashMap<String, String>,
    
    /// Generation timestamp
    pub timestamp: u64,
}

/// Architecture generation methods
#[derive(Debug, Clone, Copy)]
pub enum GenerationMethod {
    Random, Mutation, Crossover, GradientBased, 
    Bayesian, Reinforcement, Custom,
}

/// Evaluation result for an architecture
#[derive(Debug, Clone)]
pub struct EvaluationResult<T: Float + Debug + Send + Sync + 'static> {
    /// Architecture identifier
    pub architecture_id: String,
    
    /// Performance metrics
    pub metrics: HashMap<String, T>,
    
    /// Resource usage
    pub resource_usage: ResourceUsage<T>,
    
    /// Evaluation metadata
    pub metadata: EvaluationMetadata<T>,
    
    /// Success status
    pub success: bool,
    
    /// Error information (if any)
    pub error: Option<String>,
}

/// Resource usage information
#[derive(Debug, Clone)]
pub struct ResourceUsage<T: Float + Debug + Send + Sync + 'static> {
    /// Training time (seconds)
    pub training_time: Option<T>,
    
    /// Inference time (milliseconds)
    pub inference_time: Option<T>,
    
    /// Memory usage (bytes)
    pub memory_usage: Option<usize>,
    
    /// FLOPS estimate
    pub flops: Option<u64>,
    
    /// Parameter count
    pub parameter_count: Option<usize>,
}

/// Evaluation metadata
#[derive(Debug, Clone)]
pub struct EvaluationMetadata<T: Float + Debug + Send + Sync + 'static> {
    /// Evaluation method used
    pub method: EvaluationMethod,
    
    /// Evaluation timestamp
    pub timestamp: u64,
    
    /// Evaluation duration
    pub duration: T,
    
    /// Hardware information
    pub hardware_info: HashMap<String, String>,
    
    /// Additional context
    pub context: HashMap<String, String>,
}

/// Search results
#[derive(Debug, Clone)]
pub struct SearchResults<T: Float + Debug + Send + Sync + 'static> {
    /// Best architectures found
    pub best_architectures: Vec<CandidateArchitecture<T>>,
    
    /// All evaluation results
    pub all_results: Vec<EvaluationResult<T>>,
    
    /// Pareto front (for multi-objective)
    pub pareto_front: Option<Vec<CandidateArchitecture<T>>>,
    
    /// Search convergence history
    pub convergence_history: Vec<T>,
    
    /// Final statistics
    pub final_statistics: HashMap<String, f64>,
}

/// Search statistics
#[derive(Debug, Clone)]
pub struct SearchStatistics<T: Float + Debug + Send + Sync + 'static> {
    /// Total iterations completed
    pub iterations_completed: usize,
    
    /// Total evaluations performed
    pub evaluations_performed: usize,
    
    /// Total search time (seconds)
    pub total_search_time: T,
    
    /// Best performance achieved
    pub best_performance: T,
    
    /// Performance improvement over time
    pub performance_history: Vec<T>,
    
    /// Resource utilization statistics
    pub resource_stats: HashMap<String, f64>,
    
    /// Strategy-specific statistics
    pub strategy_stats: HashMap<String, f64>,
}

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> AdaptiveNASSystem<T> {
    /// Create new adaptive NAS system
    pub fn new(config: AdaptiveNASConfig<T>) -> Result<Self> {
        // Initialize search space
        let search_space = SearchSpace::new(config.search_strategy.search_space_config.clone())?;
        
        // Initialize architecture encoder
        let encoder = ArchitectureEncoder::new(config.encoding_config.architecture_encoding.clone())?;
        
        // Initialize architecture mutator
        let mutator = ArchitectureMutator::new(config.encoding_config.mutation_config.clone())?;
        
        // Initialize search strategy
        let strategy = Self::create_search_strategy(&config.search_strategy)?;
        
        // Initialize evaluator
        let evaluator = Self::create_evaluator(&config.evaluation_config)?;
        
        Ok(Self {
            config,
            current_strategy: strategy,
            evaluator,
            encoder,
            search_space,
            mutator,
            results: SearchResults {
                best_architectures: Vec::new(),
                all_results: Vec::new(),
                pareto_front: None,
                convergence_history: Vec::new(),
                final_statistics: HashMap::new(),
            },
            statistics: SearchStatistics {
                iterations_completed: 0,
                evaluations_performed: 0,
                total_search_time: T::zero(),
                best_performance: scirs2_core::numeric::NumCast::from(f64::NEG_INFINITY).unwrap_or_else(|| T::zero()),
                performance_history: Vec::new(),
                resource_stats: HashMap::new(),
                strategy_stats: HashMap::new(),
            },
        })
    }
    
    /// Create search strategy based on configuration
    fn create_search_strategy(config: &SearchStrategyConfig<T>) -> Result<Box<dyn NASSearchStrategy<T>>> {
        match config.primary_strategy {
            SearchStrategy::Evolutionary => {
                let evo_config = EvolutionaryConfig::default();
                let searcher = EvolutionarySearcher::new(evo_config)?;
                Ok(Box::new(NASEvolutionaryStrategy::new(searcher)))
            }
            SearchStrategy::BayesianOptimization => {
                let bo_config = BayesianOptConfig::default();
                let searcher = BayesianArchitectureOptimizer::new(bo_config)?;
                Ok(Box::new(NASBayesianStrategy::new(searcher)))
            }
            _ => {
                Err(OptimError::UnsupportedOperation(
                    format!("Search strategy {:?} not yet implemented", config.primary_strategy)
                ))
            }
        }
    }
    
    /// Create evaluator based on configuration
    fn create_evaluator(config: &EvaluationConfig<T>) -> Result<Box<dyn ArchitectureEvaluator<T>>> {
        // For now, create a composite evaluator
        Ok(Box::new(CompositeEvaluator::new(config.clone())?))
    }
    
    /// Run the architecture search
    pub fn search(&mut self) -> Result<SearchResults<T>> {
        let start_time = std::time::Instant::now();
        
        // Initialize strategy with search space
        self.current_strategy.initialize(&self.search_space)?;
        
        // Main search loop
        while !self.should_terminate()? {
            // Generate candidate architectures
            let candidates = self.current_strategy.generate_candidates()?;
            
            // Evaluate candidates
            let evaluation_results = self.evaluator.batch_evaluate(&candidates)?;
            
            // Update strategy with results
            self.current_strategy.update_with_results(&evaluation_results)?;
            
            // Update search results and statistics
            self.update_results(&candidates, &evaluation_results)?;
            self.update_statistics()?;
            
            // Check termination criteria
            if self.current_strategy.should_terminate() {
                break;
            }
            
            self.statistics.iterations_completed += 1;
        }
        
        self.statistics.total_search_time = T::from(start_time.elapsed().as_secs_f64()).expect("unwrap failed");
        
        Ok(self.results.clone())
    }
    
    /// Check if search should terminate
    fn should_terminate(&self) -> Result<bool> {
        // Check maximum iterations
        if let Some(max_iter) = self.config.termination_criteria.max_iterations {
            if self.statistics.iterations_completed >= max_iter {
                return Ok(true);
            }
        }
        
        // Check maximum evaluations
        if let Some(max_eval) = self.config.termination_criteria.max_evaluations {
            if self.statistics.evaluations_performed >= max_eval {
                return Ok(true);
            }
        }
        
        // Check maximum time
        if let Some(max_time) = self.config.termination_criteria.max_time {
            if self.statistics.total_search_time.to_f64().unwrap_or(0.0) >= max_time as f64 {
                return Ok(true);
            }
        }
        
        // Check target performance
        if let Some(target) = self.config.termination_criteria.target_performance {
            if self.statistics.best_performance >= target {
                return Ok(true);
            }
        }
        
        Ok(false)
    }
    
    /// Update search results
    fn update_results(&mut self, candidates: &[CandidateArchitecture<T>], 
                     results: &[EvaluationResult<T>]) -> Result<()> {
        // Add to all results
        self.results.all_results.extend_from_slice(results);
        
        // Update best architectures
        for (candidate, result) in candidates.iter().zip(results.iter()) {
            if let Some(performance) = result.metrics.get("performance") {
                if *performance > self.statistics.best_performance {
                    self.statistics.best_performance = *performance;
                    self.results.best_architectures.clear();
                    self.results.best_architectures.push(candidate.clone());
                } else if *performance == self.statistics.best_performance {
                    self.results.best_architectures.push(candidate.clone());
                }
            }
        }
        
        // Update convergence history
        self.results.convergence_history.push(self.statistics.best_performance);
        
        Ok(())
    }
    
    /// Update search statistics
    fn update_statistics(&mut self) -> Result<()> {
        self.statistics.evaluations_performed = self.results.all_results.len();
        
        // Update performance history
        self.statistics.performance_history.push(self.statistics.best_performance);
        
        // Update strategy statistics
        let strategy_stats = self.current_strategy.get_statistics();
        self.statistics.strategy_stats.extend(strategy_stats);
        
        Ok(())
    }
    
    /// Get current search results
    pub fn get_results(&self) -> &SearchResults<T> {
        &self.results
    }
    
    /// Get search statistics
    pub fn get_statistics(&self) -> &SearchStatistics<T> {
        &self.statistics
    }
    
    /// Get best architecture found so far
    pub fn get_best_architecture(&self) -> Option<&CandidateArchitecture<T>> {
        self.results.best_architectures.first()
    }
}

// Strategy adapters to integrate with existing search implementations

/// Evolutionary strategy adapter
#[derive(Debug)]
struct NASEvolutionaryStrategy<T: Float + Debug + Send + Sync + 'static> {
    searcher: EvolutionarySearcher<T>,
}

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> NASEvolutionaryStrategy<T> {
    fn new(searcher: EvolutionarySearcher<T>) -> Self {
        Self { searcher }
    }
}

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> NASSearchStrategy<T> for NASEvolutionaryStrategy<T> {
    fn initialize(&mut self, _search_space: &SearchSpace<T>) -> Result<()> {
        self.searcher.initialize_population()?;
        Ok(())
    }
    
    fn generate_candidates(&mut self) -> Result<Vec<CandidateArchitecture<T>>> {
        // This is a simplified implementation
        // In practice, would convert between different architecture representations
        Ok(vec![])
    }
    
    fn update_with_results(&mut self, _results: &[EvaluationResult<T>]) -> Result<()> {
        // Update evolutionary searcher with results
        Ok(())
    }
    
    fn should_terminate(&self) -> bool {
        // Check evolutionary-specific termination criteria
        false
    }
    
    fn get_statistics(&self) -> HashMap<String, f64> {
        HashMap::new()
    }
}

/// Bayesian optimization strategy adapter
#[derive(Debug)]
struct NASBayesianStrategy<T: Float + Debug + Send + Sync + 'static> {
    optimizer: BayesianArchitectureOptimizer<T>,
}

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> NASBayesianStrategy<T> {
    fn new(optimizer: BayesianArchitectureOptimizer<T>) -> Self {
        Self { optimizer }
    }
}

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> NASSearchStrategy<T> for NASBayesianStrategy<T> {
    fn initialize(&mut self, _search_space: &SearchSpace<T>) -> Result<()> {
        // Initialize Bayesian optimizer
        Ok(())
    }
    
    fn generate_candidates(&mut self) -> Result<Vec<CandidateArchitecture<T>>> {
        // Generate candidates using Bayesian optimization
        Ok(vec![])
    }
    
    fn update_with_results(&mut self, _results: &[EvaluationResult<T>]) -> Result<()> {
        // Update Bayesian optimizer with results
        Ok(())
    }
    
    fn should_terminate(&self) -> bool {
        false
    }
    
    fn get_statistics(&self) -> HashMap<String, f64> {
        HashMap::new()
    }
}

/// Composite evaluator that combines multiple evaluation methods
#[derive(Debug)]
struct CompositeEvaluator<T: Float + Debug + Send + Sync + 'static> {
    config: EvaluationConfig<T>,
    performance_estimator: Option<PerformanceEstimator<T>>,
    multi_objective_evaluator: Option<MultiObjectiveEvaluator<T>>,
    hardware_aware_evaluator: Option<HardwareAwareEvaluator<T>>,
}

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> CompositeEvaluator<T> {
    fn new(config: EvaluationConfig<T>) -> Result<Self> {
        let performance_estimator = if config.evaluation_methods.contains(&EvaluationMethod::PerformanceEstimation) {
            Some(PerformanceEstimator::new(config.performance_estimation.clone()))
        } else {
            None
        };
        
        let multi_objective_evaluator = if config.evaluation_methods.contains(&EvaluationMethod::MultiObjective) {
            if let Some(ref mo_config) = config.multi_objective {
                Some(MultiObjectiveEvaluator::new(mo_config.clone())?)
            } else {
                None
            }
        } else {
            None
        };
        
        let hardware_aware_evaluator = if config.evaluation_methods.contains(&EvaluationMethod::HardwareAware) {
            if let Some(ref ha_config) = config.hardware_aware {
                Some(HardwareAwareEvaluator::new(ha_config.clone()))
            } else {
                None
            }
        } else {
            None
        };
        
        Ok(Self {
            config,
            performance_estimator,
            multi_objective_evaluator,
            hardware_aware_evaluator,
        })
    }
}

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> ArchitectureEvaluator<T> for CompositeEvaluator<T> {
    fn evaluate(&mut self, architecture: &CandidateArchitecture<T>) -> Result<EvaluationResult<T>> {
        let mut metrics = HashMap::new();
        let mut resource_usage = ResourceUsage {
            training_time: None,
            inference_time: None,
            memory_usage: None,
            flops: None,
            parameter_count: None,
        };
        
        // Simplified evaluation - in practice would use actual architecture data
        metrics.insert("performance".to_string(), scirs2_core::numeric::NumCast::from(0.8).unwrap_or_else(|| T::zero()));
        
        Ok(EvaluationResult {
            architecture_id: architecture.id.clone(),
            metrics,
            resource_usage,
            metadata: EvaluationMetadata {
                method: EvaluationMethod::PerformanceEstimation,
                timestamp: std::time::SystemTime::now()
                    .duration_since(std::time::UNIX_EPOCH)
                    .unwrap_or_default()
                    .as_secs(),
                duration: scirs2_core::numeric::NumCast::from(1.0).unwrap_or_else(|| T::zero()),
                hardware_info: HashMap::new(),
                context: HashMap::new(),
            },
            success: true,
            error: None,
        })
    }
    
    fn batch_evaluate(&mut self, architectures: &[CandidateArchitecture<T>]) -> Result<Vec<EvaluationResult<T>>> {
        let mut results = Vec::new();
        for architecture in architectures {
            results.push(self.evaluate(architecture)?);
        }
        Ok(results)
    }
    
    fn get_evaluation_statistics(&self) -> HashMap<String, f64> {
        HashMap::new()
    }
}

// Default configurations

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> Default for AdaptiveNASConfig<T> {
    fn default() -> Self {
        Self {
            search_strategy: SearchStrategyConfig {
                primary_strategy: SearchStrategy::Evolutionary,
                strategy_params: HashMap::new(),
                hybrid_config: None,
                search_space_config: SearchSpaceConfig::default(),
                population_size: 20,
                max_iterations: 100,
            },
            evaluation_config: EvaluationConfig {
                evaluation_methods: vec![EvaluationMethod::PerformanceEstimation],
                performance_estimation: PerformanceEstimatorConfig::default(),
                multi_objective: None,
                hardware_aware: None,
                early_stopping: EarlyStoppingConfig {
                    enabled: true,
                    improvement_threshold: scirs2_core::numeric::NumCast::from(0.001).unwrap_or_else(|| T::zero()),
                    patience: 10,
                    min_evaluations: 5,
                },
            },
            encoding_config: EncodingConfig {
                architecture_encoding: ArchitectureEncodingConfig::default(),
                mutation_config: MutationConfig::default(),
                crossover_config: None,
                validation_config: ValidationConfig {
                    enabled: true,
                    strictness: ValidationStrictness::Standard,
                    custom_rules: Vec::new(),
                },
            },
            performance_requirements: PerformanceRequirements {
                min_accuracy: Some(scirs2_core::numeric::NumCast::from(0.9).unwrap_or_else(|| T::zero())),
                max_training_time: Some(scirs2_core::numeric::NumCast::from(3600.0).unwrap_or_else(|| T::zero())), // 1 hour
                max_inference_latency: Some(scirs2_core::numeric::NumCast::from(100.0).unwrap_or_else(|| T::zero())), // 100ms
                max_memory_usage: Some(2_000_000_000), // 2GB
                target_metrics: HashMap::new(),
            },
            resource_constraints: ResourceConstraints {
                max_compute_budget: Some(scirs2_core::numeric::NumCast::from(1000.0).unwrap_or_else(|| T::zero())),
                max_wall_time: Some(7200), // 2 hours
                max_evaluations: Some(1000),
                memory_limits: Some(4_000_000_000), // 4GB
                parallelism_limits: ParallelismLimits {
                    max_parallel_evaluations: 4,
                    max_parallel_searches: 2,
                    gpu_limits: None,
                },
            },
            termination_criteria: TerminationCriteria {
                max_iterations: Some(100),
                max_evaluations: Some(1000),
                max_time: Some(7200), // 2 hours
                target_performance: Some(scirs2_core::numeric::NumCast::from(0.95).unwrap_or_else(|| T::zero())),
                convergence: Some(ConvergenceCriteria {
                    improvement_threshold: scirs2_core::numeric::NumCast::from(0.001).unwrap_or_else(|| T::zero()),
                    patience: 10,
                    diversity_threshold: Some(scirs2_core::numeric::NumCast::from(0.1).unwrap_or_else(|| T::zero())),
                    relative_tolerance: scirs2_core::numeric::NumCast::from(1e-6).unwrap_or_else(|| T::zero()),
                }),
            },
        }
    }
}