oxirs_embed/

neuro_evolution.rs

//! Neuro-Evolution for Automated Neural Architecture Search
//!
//! This module implements cutting-edge evolutionary algorithms for automatically
//! discovering optimal neural network architectures for knowledge graph embeddings.
//!
//! Key innovations:
//! - Multi-objective evolutionary optimization (accuracy vs. efficiency)
//! - Hierarchical architecture encoding with genetic programming
//! - Co-evolution of architectures and hyperparameters
//! - Hardware-aware architecture search with efficiency constraints
//! - Progressive complexity evolution with diversity preservation

use anyhow::Result;
#[allow(unused_imports)]
use scirs2_core::random::{Random, Rng};
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, HashSet};
use std::fmt;
use uuid::Uuid;

/// Configuration for neuro-evolution
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NeuroEvolutionConfig {
    /// Population size
    pub population_size: usize,
    /// Number of generations
    pub num_generations: usize,
    /// Mutation rate
    pub mutation_rate: f64,
    /// Crossover rate
    pub crossover_rate: f64,
    /// Selection pressure
    pub selection_pressure: f64,
    /// Elite ratio (percentage of best individuals to preserve)
    pub elite_ratio: f64,
    /// Tournament size for selection
    pub tournament_size: usize,
    /// Maximum architecture depth
    pub max_depth: usize,
    /// Maximum architecture width
    pub max_width: usize,
    /// Diversity threshold
    pub diversity_threshold: f64,
    /// Hardware constraints
    pub hardware_constraints: HardwareConstraints,
    /// Multi-objective weights
    pub objective_weights: ObjectiveWeights,
    /// Complexity penalty
    pub complexity_penalty: f64,
}

impl Default for NeuroEvolutionConfig {
    fn default() -> Self {
        Self {
            population_size: 50,
            num_generations: 100,
            mutation_rate: 0.1,
            crossover_rate: 0.8,
            selection_pressure: 2.0,
            elite_ratio: 0.1,
            tournament_size: 3,
            max_depth: 10,
            max_width: 512,
            diversity_threshold: 0.7,
            hardware_constraints: HardwareConstraints::default(),
            objective_weights: ObjectiveWeights::default(),
            complexity_penalty: 0.01,
        }
    }
}

/// Hardware constraints for architecture search
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HardwareConstraints {
    /// Maximum memory usage in MB
    pub max_memory_mb: usize,
    /// Maximum inference time in ms
    pub max_inference_time_ms: f64,
    /// Maximum model parameters
    pub max_parameters: usize,
    /// Maximum FLOPs
    pub max_flops: usize,
    /// Target hardware platform
    pub target_platform: HardwarePlatform,
}

impl Default for HardwareConstraints {
    fn default() -> Self {
        Self {
            max_memory_mb: 8192, // 8GB
            max_inference_time_ms: 100.0,
            max_parameters: 10_000_000, // 10M parameters
            max_flops: 1_000_000_000,   // 1B FLOPs
            target_platform: HardwarePlatform::GPU,
        }
    }
}

/// Target hardware platforms
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum HardwarePlatform {
    CPU,
    GPU,
    TPU,
    Mobile,
    Edge,
}

/// Multi-objective optimization weights
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ObjectiveWeights {
    /// Accuracy weight
    pub accuracy: f64,
    /// Efficiency weight (speed)
    pub efficiency: f64,
    /// Memory weight
    pub memory: f64,
    /// Generalization weight
    pub generalization: f64,
    /// Robustness weight
    pub robustness: f64,
}

impl Default for ObjectiveWeights {
    fn default() -> Self {
        Self {
            accuracy: 0.4,
            efficiency: 0.3,
            memory: 0.1,
            generalization: 0.1,
            robustness: 0.1,
        }
    }
}

/// Neural architecture representation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NeuralArchitecture {
    /// Unique identifier
    pub id: Uuid,
    /// Architecture layers
    pub layers: Vec<ArchitectureLayer>,
    /// Skip connections
    pub skip_connections: Vec<SkipConnection>,
    /// Hyperparameters
    pub hyperparameters: ArchitectureHyperparameters,
    /// Performance metrics
    pub performance: Option<PerformanceMetrics>,
    /// Architecture complexity
    pub complexity: ArchitectureComplexity,
}

impl NeuralArchitecture {
    /// Create a new random architecture
    pub fn random(config: &NeuroEvolutionConfig, rng: &mut Random) -> Self {
        let mut layers = Vec::new();
        let depth = rng.random_range(1..config.max_depth + 1);

        for i in 0..depth {
            let layer = ArchitectureLayer::random(config, i, rng);
            layers.push(layer);
        }

        let skip_connections = Self::generate_skip_connections(&layers, rng);
        let hyperparameters = ArchitectureHyperparameters::random(rng);
        let complexity = Self::calculate_complexity(&layers, &skip_connections);

        Self {
            id: Uuid::new_v4(),
            layers,
            skip_connections,
            hyperparameters,
            performance: None,
            complexity,
        }
    }

    /// Generate skip connections
    fn generate_skip_connections(
        layers: &[ArchitectureLayer],
        rng: &mut Random,
    ) -> Vec<SkipConnection> {
        let mut connections = Vec::new();

        for i in 0..layers.len() {
            for j in (i + 2)..layers.len() {
                if rng.random_bool_with_chance(0.2) {
                    // 20% chance of skip connection
                    connections.push(SkipConnection {
                        from_layer: i,
                        to_layer: j,
                        connection_type: SkipConnectionType::random(rng),
                    });
                }
            }
        }

        connections
    }

    /// Calculate architecture complexity
    fn calculate_complexity(
        layers: &[ArchitectureLayer],
        skip_connections: &[SkipConnection],
    ) -> ArchitectureComplexity {
        let mut parameters = 0;
        let mut flops = 0;
        let mut memory_mb = 0;

        for layer in layers {
            parameters += layer.estimate_parameters();
            flops += layer.estimate_flops();
            memory_mb += layer.estimate_memory_mb();
        }

        // Add skip connection overhead
        for _conn in skip_connections {
            parameters += 1000; // Approximate overhead
            flops += 10000;
            memory_mb += 1;
        }

        ArchitectureComplexity {
            parameters,
            flops,
            memory_mb,
            depth: layers.len(),
            width: layers.iter().map(|l| l.output_size).max().unwrap_or(0),
        }
    }

    /// Mutate the architecture
    pub fn mutate(&mut self, config: &NeuroEvolutionConfig, rng: &mut Random) {
        if rng.random_bool_with_chance(config.mutation_rate) {
            match rng.random_range(0..4) {
                0 => self.mutate_layers(config, rng),
                1 => self.mutate_skip_connections(rng),
                2 => self.mutate_hyperparameters(rng),
                3 => self.mutate_layer_parameters(rng),
                _ => unreachable!(),
            }

            // Recalculate complexity
            self.complexity = Self::calculate_complexity(&self.layers, &self.skip_connections);
        }
    }

    /// Mutate layers
    fn mutate_layers(&mut self, config: &NeuroEvolutionConfig, rng: &mut Random) {
        match rng.random_range(0..3) {
            0 => {
                // Add layer
                if self.layers.len() < config.max_depth {
                    let position = rng.random_range(0..self.layers.len() + 1);
                    let layer = ArchitectureLayer::random(config, position, rng);
                    self.layers.insert(position, layer);
                }
            }
            1 => {
                // Remove layer
                if self.layers.len() > 1 {
                    let position = rng.random_range(0..self.layers.len());
                    self.layers.remove(position);
                }
            }
            2 => {
                // Modify existing layer
                if !self.layers.is_empty() {
                    let position = rng.random_range(0..self.layers.len());
                    self.layers[position].mutate(config, rng);
                }
            }
            _ => unreachable!(),
        }
    }

    /// Mutate skip connections
    fn mutate_skip_connections(&mut self, rng: &mut Random) {
        match rng.random_range(0..3) {
            0 => {
                // Add skip connection
                if self.layers.len() >= 3 {
                    let from = rng.random_range(0..self.layers.len() - 2);
                    let to = rng.random_range(from + 2..self.layers.len());
                    let connection = SkipConnection {
                        from_layer: from,
                        to_layer: to,
                        connection_type: SkipConnectionType::random(rng),
                    };
                    self.skip_connections.push(connection);
                }
            }
            1 => {
                // Remove skip connection
                if !self.skip_connections.is_empty() {
                    let position = rng.random_range(0..self.skip_connections.len());
                    self.skip_connections.remove(position);
                }
            }
            2 => {
                // Modify skip connection
                if !self.skip_connections.is_empty() {
                    let position = rng.random_range(0..self.skip_connections.len());
                    self.skip_connections[position].connection_type =
                        SkipConnectionType::random(rng);
                }
            }
            _ => unreachable!(),
        }
    }

    /// Mutate hyperparameters
    fn mutate_hyperparameters(&mut self, rng: &mut Random) {
        self.hyperparameters.mutate(rng);
    }

    /// Mutate layer parameters
    fn mutate_layer_parameters(&mut self, rng: &mut Random) {
        if !self.layers.is_empty() {
            let layer_idx = rng.random_range(0..self.layers.len());
            self.layers[layer_idx].mutate_parameters(rng);
        }
    }

    /// Crossover with another architecture
    pub fn crossover(&self, other: &Self, rng: &mut Random) -> (Self, Self) {
        let min_layers = self.layers.len().min(other.layers.len());
        let crossover_point = if min_layers <= 1 {
            0 // No crossover possible with 0 or 1 layers
        } else {
            rng.random_range(1..min_layers)
        };

        let mut child1_layers = self.layers[..crossover_point].to_vec();
        child1_layers.extend_from_slice(&other.layers[crossover_point..]);

        let mut child2_layers = other.layers[..crossover_point].to_vec();
        child2_layers.extend_from_slice(&self.layers[crossover_point..]);

        let child1 = Self {
            id: Uuid::new_v4(),
            layers: child1_layers,
            skip_connections: self.skip_connections.clone(),
            hyperparameters: self.hyperparameters.crossover(&other.hyperparameters, rng),
            performance: None,
            complexity: ArchitectureComplexity::default(),
        };

        let child2 = Self {
            id: Uuid::new_v4(),
            layers: child2_layers,
            skip_connections: other.skip_connections.clone(),
            hyperparameters: other.hyperparameters.crossover(&self.hyperparameters, rng),
            performance: None,
            complexity: ArchitectureComplexity::default(),
        };

        (child1, child2)
    }

    /// Calculate diversity distance to another architecture
    pub fn diversity_distance(&self, other: &Self) -> f64 {
        let layer_distance = self.layer_distance(other);
        let connection_distance = self.connection_distance(other);
        let hyperparameter_distance = self.hyperparameters.distance(&other.hyperparameters);

        (layer_distance + connection_distance + hyperparameter_distance) / 3.0
    }

    /// Calculate layer structure distance
    fn layer_distance(&self, other: &Self) -> f64 {
        let max_len = self.layers.len().max(other.layers.len());
        if max_len == 0 {
            return 0.0;
        }

        let mut differences = 0;
        for i in 0..max_len {
            match (self.layers.get(i), other.layers.get(i)) {
                (Some(l1), Some(l2)) => {
                    if l1.layer_type != l2.layer_type || l1.output_size != l2.output_size {
                        differences += 1;
                    }
                }
                (None, Some(_)) | (Some(_), None) => differences += 1,
                (None, None) => continue,
            }
        }

        differences as f64 / max_len as f64
    }

    /// Calculate skip connection distance
    fn connection_distance(&self, other: &Self) -> f64 {
        let max_connections = self
            .skip_connections
            .len()
            .max(other.skip_connections.len());
        if max_connections == 0 {
            return 0.0;
        }

        let self_set: HashSet<_> = self
            .skip_connections
            .iter()
            .map(|c| (c.from_layer, c.to_layer))
            .collect();

        let other_set: HashSet<_> = other
            .skip_connections
            .iter()
            .map(|c| (c.from_layer, c.to_layer))
            .collect();

        let intersection = self_set.intersection(&other_set).count();
        let union = self_set.union(&other_set).count();

        1.0 - (intersection as f64 / union as f64)
    }
}

/// Individual layer in the architecture
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub struct ArchitectureLayer {
    /// Layer type
    pub layer_type: LayerType,
    /// Input size
    pub input_size: usize,
    /// Output size
    pub output_size: usize,
    /// Layer-specific parameters
    pub parameters: LayerParameters,
}

impl ArchitectureLayer {
    /// Create a random layer
    pub fn random(config: &NeuroEvolutionConfig, layer_index: usize, rng: &mut Random) -> Self {
        let layer_type = LayerType::random(rng);
        let output_size = rng.random_range(16..config.max_width + 1);
        let input_size = if layer_index == 0 {
            128
        } else {
            output_size / 2
        };
        let parameters = LayerParameters::random(&layer_type, rng);

        Self {
            layer_type,
            input_size,
            output_size,
            parameters,
        }
    }

    /// Mutate the layer
    pub fn mutate(&mut self, config: &NeuroEvolutionConfig, rng: &mut Random) {
        match rng.random_range(0..3) {
            0 => self.layer_type = LayerType::random(rng),
            1 => self.output_size = rng.random_range(16..config.max_width + 1),
            2 => self.parameters.mutate(rng),
            _ => unreachable!(),
        }
    }

    /// Mutate layer parameters only
    pub fn mutate_parameters(&mut self, rng: &mut Random) {
        self.parameters.mutate(rng);
    }

    /// Estimate number of parameters
    pub fn estimate_parameters(&self) -> usize {
        match self.layer_type {
            LayerType::Dense => self.input_size * self.output_size + self.output_size,
            LayerType::Attention => {
                let head_dim = self.output_size / 8; // Assume 8 heads
                self.input_size * self.output_size * 3 + self.output_size * head_dim
            }
            LayerType::Convolution => {
                let kernel_size = 3; // Assume 3x3 kernels
                kernel_size * kernel_size * self.input_size * self.output_size + self.output_size
            }
            LayerType::GraphConv => self.input_size * self.output_size + self.output_size,
            LayerType::LSTM => {
                4 * (self.input_size * self.output_size + self.output_size * self.output_size)
            }
            LayerType::Transformer => {
                let ff_size = self.output_size * 4;
                self.input_size * self.output_size * 3
                    + self.output_size * ff_size
                    + ff_size * self.output_size
            }
            LayerType::Embedding => self.input_size * self.output_size,
        }
    }

    /// Estimate FLOPs
    pub fn estimate_flops(&self) -> usize {
        match self.layer_type {
            LayerType::Dense => self.input_size * self.output_size * 2,
            LayerType::Attention => self.input_size * self.output_size * 6,
            LayerType::Convolution => {
                let kernel_size = 3;
                kernel_size * kernel_size * self.input_size * self.output_size * 2
            }
            LayerType::GraphConv => self.input_size * self.output_size * 3,
            LayerType::LSTM => self.input_size * self.output_size * 8,
            LayerType::Transformer => self.input_size * self.output_size * 12,
            LayerType::Embedding => self.input_size,
        }
    }

    /// Estimate memory usage in MB
    pub fn estimate_memory_mb(&self) -> usize {
        let params = self.estimate_parameters();
        let activations = self.output_size * 1000; // Assume batch size of 1000
        (params + activations) * 4 / (1024 * 1024) // 4 bytes per float, convert to MB
    }
}

/// Types of neural network layers
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum LayerType {
    Dense,
    Attention,
    Convolution,
    GraphConv,
    LSTM,
    Transformer,
    Embedding,
}

impl LayerType {
    /// Generate random layer type
    pub fn random(rng: &mut Random) -> Self {
        match rng.random_range(0..7) {
            0 => LayerType::Dense,
            1 => LayerType::Attention,
            2 => LayerType::Convolution,
            3 => LayerType::GraphConv,
            4 => LayerType::LSTM,
            5 => LayerType::Transformer,
            6 => LayerType::Embedding,
            _ => unreachable!(),
        }
    }
}

/// Layer-specific parameters
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub struct LayerParameters {
    /// Activation function
    pub activation: ActivationFunction,
    /// Dropout rate
    pub dropout: f64,
    /// Normalization type
    pub normalization: NormalizationType,
    /// Layer-specific settings
    pub settings: HashMap<String, f64>,
}

impl LayerParameters {
    /// Create random parameters
    pub fn random(layer_type: &LayerType, rng: &mut Random) -> Self {
        let activation = ActivationFunction::random(rng);
        let dropout = rng.gen_range(0.0..0.5);
        let normalization = NormalizationType::random(rng);
        let mut settings = HashMap::new();

        match layer_type {
            LayerType::Attention => {
                settings.insert("num_heads".to_string(), rng.random_range(1..16) as f64);
                settings.insert("head_dim".to_string(), rng.random_range(32..128) as f64);
            }
            LayerType::Convolution => {
                settings.insert("kernel_size".to_string(), rng.random_range(1..7) as f64);
                settings.insert("stride".to_string(), rng.random_range(1..3) as f64);
            }
            LayerType::LSTM => {
                settings.insert(
                    "bidirectional".to_string(),
                    if rng.random_bool_with_chance(0.5) {
                        1.0
                    } else {
                        0.0
                    },
                );
            }
            _ => {}
        }

        Self {
            activation,
            dropout,
            normalization,
            settings,
        }
    }

    /// Mutate parameters
    pub fn mutate(&mut self, rng: &mut Random) {
        match rng.random_range(0..4) {
            0 => self.activation = ActivationFunction::random(rng),
            1 => self.dropout = rng.gen_range(0.0..0.5),
            2 => self.normalization = NormalizationType::random(rng),
            3 => {
                // Mutate settings
                for value in self.settings.values_mut() {
                    if rng.random_bool_with_chance(0.3) {
                        *value *= rng.gen_range(0.8..1.2);
                    }
                }
            }
            _ => unreachable!(),
        }
    }
}

/// Activation functions
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum ActivationFunction {
    ReLU,
    GELU,
    Swish,
    Tanh,
    Sigmoid,
    LeakyReLU,
    ELU,
}

impl ActivationFunction {
    pub fn random(rng: &mut Random) -> Self {
        match rng.random_range(0..7) {
            0 => ActivationFunction::ReLU,
            1 => ActivationFunction::GELU,
            2 => ActivationFunction::Swish,
            3 => ActivationFunction::Tanh,
            4 => ActivationFunction::Sigmoid,
            5 => ActivationFunction::LeakyReLU,
            6 => ActivationFunction::ELU,
            _ => unreachable!(),
        }
    }
}

/// Normalization types
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum NormalizationType {
    None,
    LayerNorm,
    BatchNorm,
    GroupNorm,
    RMSNorm,
}

impl NormalizationType {
    pub fn random(rng: &mut Random) -> Self {
        match rng.random_range(0..5) {
            0 => NormalizationType::None,
            1 => NormalizationType::LayerNorm,
            2 => NormalizationType::BatchNorm,
            3 => NormalizationType::GroupNorm,
            4 => NormalizationType::RMSNorm,
            _ => unreachable!(),
        }
    }
}

/// Skip connection types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SkipConnection {
    pub from_layer: usize,
    pub to_layer: usize,
    pub connection_type: SkipConnectionType,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SkipConnectionType {
    Add,
    Concat,
    Multiply,
    Gate,
}

impl SkipConnectionType {
    pub fn random(rng: &mut Random) -> Self {
        match rng.random_range(0..4) {
            0 => SkipConnectionType::Add,
            1 => SkipConnectionType::Concat,
            2 => SkipConnectionType::Multiply,
            3 => SkipConnectionType::Gate,
            _ => unreachable!(),
        }
    }
}

/// Architecture hyperparameters
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ArchitectureHyperparameters {
    pub learning_rate: f64,
    pub batch_size: usize,
    pub weight_decay: f64,
    pub gradient_clipping: f64,
    pub optimizer: OptimizerType,
    pub scheduler: SchedulerType,
}

impl ArchitectureHyperparameters {
    pub fn random(rng: &mut Random) -> Self {
        Self {
            learning_rate: rng.random_f64() * (1e-2 - 1e-5) + 1e-5,
            batch_size: {
                let options = [16, 32, 64, 128, 256];
                let idx = rng.random_range(0..options.len());
                options[idx]
            },
            weight_decay: rng.random_f64() * (1e-3 - 1e-6) + 1e-6,
            gradient_clipping: rng.random_f64() * (10.0 - 0.1) + 0.1,
            optimizer: OptimizerType::random(rng),
            scheduler: SchedulerType::random(rng),
        }
    }

    pub fn mutate(&mut self, rng: &mut Random) {
        match rng.random_range(0..6) {
            0 => self.learning_rate *= rng.random_f64() * (2.0 - 0.5) + 0.5,
            1 => {
                let options = [16, 32, 64, 128, 256];
                let idx = rng.random_range(0..options.len());
                self.batch_size = options[idx];
            }
            2 => self.weight_decay *= rng.random_f64() * (2.0 - 0.5) + 0.5,
            3 => self.gradient_clipping *= rng.random_f64() * (2.0 - 0.5) + 0.5,
            4 => self.optimizer = OptimizerType::random(rng),
            5 => self.scheduler = SchedulerType::random(rng),
            _ => unreachable!(),
        }
    }

    pub fn crossover(&self, other: &Self, rng: &mut Random) -> Self {
        Self {
            learning_rate: if rng.random_bool_with_chance(0.5) {
                self.learning_rate
            } else {
                other.learning_rate
            },
            batch_size: if rng.random_bool_with_chance(0.5) {
                self.batch_size
            } else {
                other.batch_size
            },
            weight_decay: if rng.random_bool_with_chance(0.5) {
                self.weight_decay
            } else {
                other.weight_decay
            },
            gradient_clipping: if rng.random_bool_with_chance(0.5) {
                self.gradient_clipping
            } else {
                other.gradient_clipping
            },
            optimizer: if rng.random_bool_with_chance(0.5) {
                self.optimizer.clone()
            } else {
                other.optimizer.clone()
            },
            scheduler: if rng.random_bool_with_chance(0.5) {
                self.scheduler.clone()
            } else {
                other.scheduler.clone()
            },
        }
    }

    pub fn distance(&self, other: &Self) -> f64 {
        let lr_diff = (self.learning_rate - other.learning_rate).abs()
            / self.learning_rate.max(other.learning_rate);
        let batch_diff = (self.batch_size as f64 - other.batch_size as f64).abs()
            / (self.batch_size as f64).max(other.batch_size as f64);
        let wd_diff = (self.weight_decay - other.weight_decay).abs()
            / self.weight_decay.max(other.weight_decay);

        (lr_diff + batch_diff + wd_diff) / 3.0
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OptimizerType {
    Adam,
    AdamW,
    SGD,
    RMSprop,
    AdaGrad,
}

impl OptimizerType {
    pub fn random(rng: &mut Random) -> Self {
        match rng.random_range(0..5) {
            0 => OptimizerType::Adam,
            1 => OptimizerType::AdamW,
            2 => OptimizerType::SGD,
            3 => OptimizerType::RMSprop,
            4 => OptimizerType::AdaGrad,
            _ => unreachable!(),
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SchedulerType {
    Constant,
    Linear,
    Cosine,
    Exponential,
    StepLR,
}

impl SchedulerType {
    pub fn random(rng: &mut Random) -> Self {
        match rng.random_range(0..5) {
            0 => SchedulerType::Constant,
            1 => SchedulerType::Linear,
            2 => SchedulerType::Cosine,
            3 => SchedulerType::Exponential,
            4 => SchedulerType::StepLR,
            _ => unreachable!(),
        }
    }
}

/// Performance metrics for evaluation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceMetrics {
    pub accuracy: f64,
    pub inference_time_ms: f64,
    pub memory_usage_mb: f64,
    pub parameter_count: usize,
    pub flops: usize,
    pub generalization_score: f64,
    pub robustness_score: f64,
    pub multi_objective_score: f64,
}

impl PerformanceMetrics {
    /// Calculate multi-objective fitness score
    pub fn calculate_fitness(&self, weights: &ObjectiveWeights, complexity_penalty: f64) -> f64 {
        let accuracy_score = self.accuracy;
        let efficiency_score = 1.0 / (1.0 + self.inference_time_ms / 100.0); // Normalize to [0,1]
        let memory_score = 1.0 / (1.0 + self.memory_usage_mb / 1000.0); // Normalize to [0,1]
        let generalization_score = self.generalization_score;
        let robustness_score = self.robustness_score;

        let weighted_score = weights.accuracy * accuracy_score
            + weights.efficiency * efficiency_score
            + weights.memory * memory_score
            + weights.generalization * generalization_score
            + weights.robustness * robustness_score;

        // Apply complexity penalty
        let complexity_factor =
            1.0 / (1.0 + complexity_penalty * self.parameter_count as f64 / 1e6);

        weighted_score * complexity_factor
    }
}

/// Architecture complexity metrics
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct ArchitectureComplexity {
    pub parameters: usize,
    pub flops: usize,
    pub memory_mb: usize,
    pub depth: usize,
    pub width: usize,
}

/// Population of neural architectures
#[derive(Debug, Clone)]
pub struct Population {
    pub individuals: Vec<NeuralArchitecture>,
    pub generation: usize,
    pub best_fitness: f64,
    pub average_fitness: f64,
    pub diversity_score: f64,
}

impl Population {
    /// Create initial random population
    pub fn initialize(config: &NeuroEvolutionConfig) -> Self {
        let mut rng = Random::default();
        let mut individuals = Vec::new();

        for _ in 0..config.population_size {
            individuals.push(NeuralArchitecture::random(config, &mut rng));
        }

        Self {
            individuals,
            generation: 0,
            best_fitness: 0.0,
            average_fitness: 0.0,
            diversity_score: 0.0,
        }
    }

    /// Evaluate population fitness
    pub async fn evaluate(&mut self, evaluator: &ArchitectureEvaluator) -> Result<()> {
        let mut total_fitness = 0.0;
        let mut best_fitness: f64 = 0.0;

        for individual in &mut self.individuals {
            let performance = evaluator.evaluate(individual).await?;
            individual.performance = Some(performance.clone());

            let fitness = performance.calculate_fitness(
                &evaluator.config.objective_weights,
                evaluator.config.complexity_penalty,
            );

            total_fitness += fitness;
            best_fitness = best_fitness.max(fitness);
        }

        self.best_fitness = best_fitness;
        self.average_fitness = total_fitness / self.individuals.len() as f64;
        self.diversity_score = self.calculate_diversity();

        Ok(())
    }

    /// Calculate population diversity
    fn calculate_diversity(&self) -> f64 {
        let mut total_distance = 0.0;
        let mut count = 0;

        for i in 0..self.individuals.len() {
            for j in (i + 1)..self.individuals.len() {
                total_distance += self.individuals[i].diversity_distance(&self.individuals[j]);
                count += 1;
            }
        }

        if count > 0 {
            total_distance / count as f64
        } else {
            0.0
        }
    }

    /// Evolve to next generation
    pub fn evolve(&mut self, config: &NeuroEvolutionConfig) -> Result<()> {
        let mut rng = Random::default();

        // Sort by fitness
        self.individuals.sort_by(|a, b| {
            let fitness_a = a
                .performance
                .as_ref()
                .expect("performance should be evaluated before evolve")
                .calculate_fitness(&config.objective_weights, config.complexity_penalty);
            let fitness_b = b
                .performance
                .as_ref()
                .expect("performance should be evaluated before evolve")
                .calculate_fitness(&config.objective_weights, config.complexity_penalty);
            fitness_b
                .partial_cmp(&fitness_a)
                .expect("fitness values should be finite")
        });

        let mut new_population = Vec::new();

        // Elite preservation
        let elite_count = (config.population_size as f64 * config.elite_ratio) as usize;
        for i in 0..elite_count {
            new_population.push(self.individuals[i].clone());
        }

        // Generate offspring
        while new_population.len() < config.population_size {
            if rng.random_bool_with_chance(config.crossover_rate) {
                // Crossover
                let parent1 = self.tournament_selection(config, &mut rng);
                let parent2 = self.tournament_selection(config, &mut rng);
                let (mut child1, mut child2) = parent1.crossover(parent2, &mut rng);

                // Mutation
                child1.mutate(config, &mut rng);
                child2.mutate(config, &mut rng);

                new_population.push(child1);
                if new_population.len() < config.population_size {
                    new_population.push(child2);
                }
            } else {
                // Mutation only
                let parent = self.tournament_selection(config, &mut rng);
                let mut child = parent.clone();
                child.id = Uuid::new_v4();
                child.mutate(config, &mut rng);
                new_population.push(child);
            }
        }

        self.individuals = new_population;
        self.generation += 1;

        Ok(())
    }

    /// Tournament selection
    fn tournament_selection(
        &self,
        config: &NeuroEvolutionConfig,
        rng: &mut Random,
    ) -> &NeuralArchitecture {
        let mut best = &self.individuals[0];
        let mut best_fitness = 0.0;

        for _ in 0..config.tournament_size {
            let candidate_idx = rng.random_range(0..self.individuals.len());
            let candidate = &self.individuals[candidate_idx];

            if let Some(ref performance) = candidate.performance {
                let fitness = performance
                    .calculate_fitness(&config.objective_weights, config.complexity_penalty);

                if fitness > best_fitness {
                    best = candidate;
                    best_fitness = fitness;
                }
            }
        }

        best
    }

    /// Get the best individual
    pub fn get_best(&self) -> Option<&NeuralArchitecture> {
        self.individuals.first()
    }
}

/// Architecture evaluator
#[derive(Debug, Clone)]
pub struct ArchitectureEvaluator {
    pub config: NeuroEvolutionConfig,
    pub evaluation_cache: HashMap<Uuid, PerformanceMetrics>,
}

impl ArchitectureEvaluator {
    pub fn new(config: NeuroEvolutionConfig) -> Self {
        Self {
            config,
            evaluation_cache: HashMap::new(),
        }
    }

    /// Evaluate a single architecture
    pub async fn evaluate(&self, architecture: &NeuralArchitecture) -> Result<PerformanceMetrics> {
        // Check cache first
        if let Some(cached) = self.evaluation_cache.get(&architecture.id) {
            return Ok(cached.clone());
        }

        // Simulate architecture evaluation
        let metrics = self.simulate_evaluation(architecture)?;

        Ok(metrics)
    }

    /// Simulate architecture evaluation (replace with actual training/evaluation)
    fn simulate_evaluation(&self, architecture: &NeuralArchitecture) -> Result<PerformanceMetrics> {
        let mut rng = Random::default();

        // Simulate based on architecture properties
        let base_accuracy = 0.7 + rng.gen_range(0.0..0.2);
        let complexity_factor = 1.0 / (1.0 + architecture.complexity.parameters as f64 / 1e6);
        let accuracy = (base_accuracy * (0.8 + 0.4 * complexity_factor)).min(1.0);

        let inference_time = 10.0 + architecture.complexity.parameters as f64 / 1e5;
        let memory_usage = architecture.complexity.memory_mb as f64;

        let generalization_score = (accuracy * (0.9 + 0.1 * rng.gen_range(0.0..1.0))).min(1.0);
        let robustness_score = (accuracy * (0.85 + 0.15 * rng.gen_range(0.0..1.0))).min(1.0);

        let metrics = PerformanceMetrics {
            accuracy,
            inference_time_ms: inference_time,
            memory_usage_mb: memory_usage,
            parameter_count: architecture.complexity.parameters,
            flops: architecture.complexity.flops,
            generalization_score,
            robustness_score,
            multi_objective_score: 0.0, // Will be calculated by fitness function
        };

        Ok(metrics)
    }

    /// Validate architecture against hardware constraints
    pub fn validate_constraints(&self, architecture: &NeuralArchitecture) -> bool {
        let constraints = &self.config.hardware_constraints;

        architecture.complexity.memory_mb <= constraints.max_memory_mb
            && architecture.complexity.parameters <= constraints.max_parameters
            && architecture.complexity.flops <= constraints.max_flops
    }
}

/// Main neuro-evolution system
#[derive(Debug, Clone)]
pub struct NeuroEvolutionSystem {
    pub config: NeuroEvolutionConfig,
    pub population: Population,
    pub evaluator: ArchitectureEvaluator,
    pub evolution_history: Vec<EvolutionStats>,
}

impl NeuroEvolutionSystem {
    /// Create new neuro-evolution system
    pub fn new(config: NeuroEvolutionConfig) -> Self {
        let population = Population::initialize(&config);
        let evaluator = ArchitectureEvaluator::new(config.clone());

        Self {
            config,
            population,
            evaluator,
            evolution_history: Vec::new(),
        }
    }

    /// Run evolution for specified number of generations
    pub async fn evolve(&mut self) -> Result<NeuralArchitecture> {
        for generation in 0..self.config.num_generations {
            // Evaluate population
            self.population.evaluate(&self.evaluator).await?;

            // Record statistics
            let stats = EvolutionStats {
                generation,
                best_fitness: self.population.best_fitness,
                average_fitness: self.population.average_fitness,
                diversity_score: self.population.diversity_score,
                best_architecture: self.population.get_best().cloned(),
            };
            self.evolution_history.push(stats);

            // Check convergence
            if self.check_convergence() {
                break;
            }

            // Evolve to next generation
            if generation < self.config.num_generations - 1 {
                self.population.evolve(&self.config)?;
            }
        }

        // Return best architecture
        self.population
            .get_best()
            .cloned()
            .ok_or_else(|| anyhow::anyhow!("No best architecture found"))
    }

    /// Check convergence criteria
    fn check_convergence(&self) -> bool {
        if self.evolution_history.len() < 10 {
            return false;
        }

        // Check if fitness improvement has stagnated
        let recent_best: Vec<f64> = self
            .evolution_history
            .iter()
            .rev()
            .take(10)
            .map(|s| s.best_fitness)
            .collect();

        let improvement = recent_best[0] - recent_best[9];
        improvement < 0.001 // Very small improvement threshold
    }

    /// Get evolution statistics
    pub fn get_stats(&self) -> &[EvolutionStats] {
        &self.evolution_history
    }
}

/// Evolution statistics for tracking progress
#[derive(Debug, Clone)]
pub struct EvolutionStats {
    pub generation: usize,
    pub best_fitness: f64,
    pub average_fitness: f64,
    pub diversity_score: f64,
    pub best_architecture: Option<NeuralArchitecture>,
}

impl fmt::Display for EvolutionStats {
    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
        write!(
            f,
            "Gen {}: Best={:.4}, Avg={:.4}, Diversity={:.4}",
            self.generation, self.best_fitness, self.average_fitness, self.diversity_score
        )
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_neuro_evolution_config() {
        let config = NeuroEvolutionConfig::default();
        assert_eq!(config.population_size, 50);
        assert_eq!(config.num_generations, 100);
        assert!(config.mutation_rate > 0.0);
    }

    #[test]
    fn test_neural_architecture_creation() {
        let config = NeuroEvolutionConfig::default();
        let mut rng = Random::default();
        let arch = NeuralArchitecture::random(&config, &mut rng);

        assert!(!arch.layers.is_empty());
        assert!(arch.layers.len() <= config.max_depth);
        assert!(arch.complexity.parameters > 0);
    }

    #[test]
    fn test_architecture_mutation() {
        let config = NeuroEvolutionConfig::default();
        let mut rng = Random::default();
        let mut arch = NeuralArchitecture::random(&config, &mut rng);
        let original_id = arch.id;

        arch.mutate(&config, &mut rng);
        assert_eq!(arch.id, original_id); // ID should not change during mutation
    }

    #[test]
    fn test_architecture_crossover() {
        let config = NeuroEvolutionConfig::default();
        let mut rng = Random::default();
        let parent1 = NeuralArchitecture::random(&config, &mut rng);
        let parent2 = NeuralArchitecture::random(&config, &mut rng);

        let (child1, child2) = parent1.crossover(&parent2, &mut rng);
        assert_ne!(child1.id, parent1.id);
        assert_ne!(child2.id, parent2.id);
        assert_ne!(child1.id, child2.id);
    }

    #[test]
    fn test_layer_parameter_estimation() {
        let layer = ArchitectureLayer {
            layer_type: LayerType::Dense,
            input_size: 128,
            output_size: 256,
            parameters: LayerParameters {
                activation: ActivationFunction::ReLU,
                dropout: 0.1,
                normalization: NormalizationType::LayerNorm,
                settings: HashMap::new(),
            },
        };

        let params = layer.estimate_parameters();
        let expected = 128 * 256 + 256; // weights + biases
        assert_eq!(params, expected);
    }

    #[test]
    fn test_population_initialization() {
        let config = NeuroEvolutionConfig::default();
        let population = Population::initialize(&config);

        assert_eq!(population.individuals.len(), config.population_size);
        assert_eq!(population.generation, 0);
    }

    #[test]
    fn test_diversity_calculation() {
        let config = NeuroEvolutionConfig::default();
        let mut rng = Random::default();
        let arch1 = NeuralArchitecture::random(&config, &mut rng);
        let arch2 = NeuralArchitecture::random(&config, &mut rng);

        let distance = arch1.diversity_distance(&arch2);
        assert!((0.0..=1.0).contains(&distance));
    }

    #[tokio::test]
    async fn test_architecture_evaluation() {
        let config = NeuroEvolutionConfig::default();
        let evaluator = ArchitectureEvaluator::new(config.clone());
        let mut rng = Random::default();
        let arch = NeuralArchitecture::random(&config, &mut rng);

        let metrics = evaluator.evaluate(&arch).await.unwrap();
        assert!(metrics.accuracy >= 0.0 && metrics.accuracy <= 1.0);
        assert!(metrics.inference_time_ms > 0.0);
    }

    #[test]
    fn test_hardware_constraints() {
        let config = NeuroEvolutionConfig::default();
        let evaluator = ArchitectureEvaluator::new(config.clone());
        let mut rng = Random::default();
        let arch = NeuralArchitecture::random(&config, &mut rng);

        let is_valid = evaluator.validate_constraints(&arch);
        // Should be valid for reasonable random architectures
        assert!(
            is_valid || arch.complexity.parameters > config.hardware_constraints.max_parameters
        );
    }

    #[tokio::test]
    async fn test_neuro_evolution_system() {
        let config = NeuroEvolutionConfig {
            population_size: 5, // Very small population for testing
            num_generations: 2, // Minimal generations for testing
            max_depth: 3,       // Limit architecture complexity
            max_width: 16,      // Limit architecture size
            ..Default::default()
        };

        let mut system = NeuroEvolutionSystem::new(config);
        let best_arch = system.evolve().await.unwrap();

        assert!(!best_arch.layers.is_empty());
        assert!(system.evolution_history.len() <= 2);
    }
}