scirs2_series/advanced_fusion_intelligence/

evolution.rs

//! Evolution and Architecture Components for Advanced Fusion Intelligence
//!
//! This module contains all evolution and neural architecture related structures
//! and implementations for the advanced fusion intelligence system, including
//! evolutionary algorithms, architecture evolution, and genetic operations.

use scirs2_core::ndarray::Array1;
use scirs2_core::numeric::{Float, FromPrimitive};
use scirs2_core::random::SeedableRng;
use std::collections::HashMap;
use std::fmt::Debug;

use crate::error::Result;

/// Engine for evolving neural architectures
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct EvolutionEngine<F: Float + Debug> {
    population: Vec<Architecture<F>>,
    selection_strategy: SelectionStrategy,
    mutation_rate: F,
    crossover_rate: F,
}

/// Neural network architecture configuration
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct Architecture<F: Float + Debug> {
    layers: Vec<LayerConfig<F>>,
    connections: Vec<ConnectionConfig<F>>,
    fitness_score: F,
}

/// Configuration for individual network layer
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct LayerConfig<F: Float + Debug> {
    layer_type: LayerType,
    size: usize,
    activation: ActivationFunction,
    parameters: Vec<F>,
}

/// Types of neural network layers
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum LayerType {
    /// Fully connected dense layer
    Dense,
    /// Convolutional layer
    Convolutional,
    /// Recurrent neural network layer
    Recurrent,
    /// Attention mechanism layer
    Attention,
    /// Quantum computing layer
    Quantum,
    /// Long Short-Term Memory layer
    LSTM,
    /// Dropout regularization layer
    Dropout,
}

/// Activation functions for neural networks
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum ActivationFunction {
    /// Rectified Linear Unit activation
    ReLU,
    /// Sigmoid activation function
    Sigmoid,
    /// Hyperbolic tangent activation
    Tanh,
    /// Gaussian Error Linear Unit
    GELU,
    /// Swish activation function
    Swish,
    /// Quantum activation function
    Quantum,
    /// Softmax activation function
    Softmax,
}

/// Configuration for layer connections
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct ConnectionConfig<F: Float + Debug> {
    from_layer: usize,
    to_layer: usize,
    connection_type: ConnectionType,
    strength: F,
    weight: F,
}

/// Types of neural network connections
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum ConnectionType {
    /// Feedforward connection
    Feedforward,
    /// Recurrent connection
    Recurrent,
    /// Skip connection
    Skip,
    /// Attention-based connection
    Attention,
    /// Quantum connection
    Quantum,
    /// Fully connected layer
    FullyConnected,
}

/// Strategies for evolutionary selection
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum SelectionStrategy {
    /// Tournament selection
    Tournament,
    /// Roulette wheel selection
    Roulette,
    /// Elite selection
    Elite,
    /// Rank-based selection
    RankBased,
}

/// Fitness evaluator for evolutionary algorithms
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct FitnessEvaluator<F: Float + Debug> {
    evaluation_function: EvaluationFunction,
    weights: Vec<F>,
    normalization_strategy: NormalizationStrategy,
}

/// Evaluation function types
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum EvaluationFunction {
    /// Accuracy-based evaluation
    Accuracy,
    /// Latency-optimized evaluation
    LatencyOptimized,
    /// Memory-optimized evaluation
    MemoryOptimized,
    /// Multi-objective evaluation
    MultiObjective,
}

/// Normalization strategies
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum NormalizationStrategy {
    /// Min-max normalization
    MinMax,
    /// Z-score normalization
    ZScore,
    /// Robust normalization
    Robust,
    /// Quantile normalization
    Quantile,
}

/// Mutation operator for evolutionary algorithms
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct MutationOperator {
    mutation_type: MutationType,
    probability: f64,
    intensity: f64,
}

/// Types of mutations for evolutionary algorithms
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum MutationType {
    /// Parameter mutation
    ParameterMutation,
    /// Structural mutation
    StructuralMutation,
    /// Layer addition
    LayerAddition,
    /// Layer removal
    LayerRemoval,
    /// Connection mutation
    ConnectionMutation,
}

/// Crossover operator for evolutionary algorithms
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct CrossoverOperator {
    crossover_type: CrossoverType,
    probability: f64,
}

/// Types of crossover operations
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum CrossoverType {
    /// Single point crossover
    SinglePoint,
    /// Two point crossover
    TwoPoint,
    /// Uniform crossover
    Uniform,
    /// Semantic crossover
    Semantic,
}

impl<F: Float + Debug + Clone + FromPrimitive> EvolutionEngine<F> {
    /// Create new evolution engine
    pub fn new(population_size: usize, selection_strategy: SelectionStrategy) -> Self {
        let mut population = Vec::with_capacity(population_size);

        // Initialize random population
        for _ in 0..population_size {
            let architecture = Architecture::random();
            population.push(architecture);
        }

        EvolutionEngine {
            population,
            selection_strategy,
            mutation_rate: F::from_f64(0.1).unwrap(),
            crossover_rate: F::from_f64(0.8).unwrap(),
        }
    }

    /// Evolve population for one generation
    pub fn evolve_generation(&mut self, fitness_evaluator: &FitnessEvaluator<F>) -> Result<()> {
        // 1. Evaluate fitness for all individuals
        self.evaluate_population(fitness_evaluator)?;

        // 2. Selection
        let selected = self.selection()?;

        // 3. Crossover and mutation
        let mut new_population = Vec::new();

        for i in (0..selected.len()).step_by(2) {
            let parent1 = &selected[i];
            let parent2 = if i + 1 < selected.len() {
                &selected[i + 1]
            } else {
                &selected[0]
            };

            // Crossover
            let (mut child1, mut child2) =
                if scirs2_core::random::random::<f64>() < self.crossover_rate.to_f64().unwrap() {
                    self.crossover(parent1, parent2)?
                } else {
                    (parent1.clone(), parent2.clone())
                };

            // Mutation
            if scirs2_core::random::random::<f64>() < self.mutation_rate.to_f64().unwrap() {
                self.mutate(&mut child1)?;
            }
            if scirs2_core::random::random::<f64>() < self.mutation_rate.to_f64().unwrap() {
                self.mutate(&mut child2)?;
            }

            new_population.push(child1);
            if new_population.len() < self.population.len() {
                new_population.push(child2);
            }
        }

        self.population = new_population;
        Ok(())
    }

    /// Evaluate fitness for entire population
    fn evaluate_population(&mut self, fitness_evaluator: &FitnessEvaluator<F>) -> Result<()> {
        for individual in &mut self.population {
            individual.fitness_score = fitness_evaluator.evaluate(individual)?;
        }
        Ok(())
    }

    /// Select parents for reproduction
    fn selection(&self) -> Result<Vec<Architecture<F>>> {
        match self.selection_strategy {
            SelectionStrategy::Tournament => self.tournament_selection(),
            SelectionStrategy::Roulette => self.roulette_wheel_selection(),
            SelectionStrategy::Elite => self.elite_selection(),
            SelectionStrategy::RankBased => self.rank_based_selection(),
        }
    }

    /// Tournament selection implementation
    fn tournament_selection(&self) -> Result<Vec<Architecture<F>>> {
        let tournament_size = 3;
        let mut selected = Vec::new();
        let mut rng = scirs2_core::random::rngs::StdRng::seed_from_u64(42);

        for _ in 0..self.population.len() {
            let mut tournament = Vec::new();

            // Select random individuals for tournament
            for _ in 0..tournament_size {
                let idx =
                    scirs2_core::random::Rng::random_range(&mut rng, 0..self.population.len());
                tournament.push(&self.population[idx]);
            }

            // Select best individual from tournament
            let winner = tournament
                .iter()
                .max_by(|a, b| a.fitness_score.partial_cmp(&b.fitness_score).unwrap())
                .unwrap();

            selected.push((*winner).clone());
        }

        Ok(selected)
    }

    /// Roulette wheel selection implementation
    fn roulette_wheel_selection(&self) -> Result<Vec<Architecture<F>>> {
        let total_fitness: F = self
            .population
            .iter()
            .map(|ind| ind.fitness_score)
            .fold(F::zero(), |acc, x| acc + x);

        if total_fitness == F::zero() {
            return Ok(self.population.clone());
        }

        let mut selected = Vec::new();

        for _ in 0..self.population.len() {
            let random_value =
                F::from_f64(scirs2_core::random::random::<f64>()).unwrap() * total_fitness;
            let mut cumulative_fitness = F::zero();

            for individual in &self.population {
                cumulative_fitness = cumulative_fitness + individual.fitness_score;
                if cumulative_fitness >= random_value {
                    selected.push(individual.clone());
                    break;
                }
            }
        }

        Ok(selected)
    }

    /// Elite selection implementation
    fn elite_selection(&self) -> Result<Vec<Architecture<F>>> {
        let mut sorted_population = self.population.clone();
        sorted_population.sort_by(|a, b| b.fitness_score.partial_cmp(&a.fitness_score).unwrap());

        // Select top 50% as elite
        let elite_size = self.population.len() / 2;
        let mut selected = Vec::new();

        // Add elite individuals twice to maintain population size
        for i in 0..self.population.len() {
            let idx = i % elite_size;
            selected.push(sorted_population[idx].clone());
        }

        Ok(selected)
    }

    /// Rank-based selection implementation
    fn rank_based_selection(&self) -> Result<Vec<Architecture<F>>> {
        let mut sorted_population = self.population.clone();
        sorted_population.sort_by(|a, b| a.fitness_score.partial_cmp(&b.fitness_score).unwrap());

        // Assign ranks (higher rank = better fitness)
        let mut selected = Vec::new();
        let total_ranks: usize = (1..=self.population.len()).sum();

        for _ in 0..self.population.len() {
            let random_value = scirs2_core::random::random::<f64>() * total_ranks as f64;
            let mut cumulative_rank = 0.0;

            for (rank, individual) in sorted_population.iter().enumerate() {
                cumulative_rank += (rank + 1) as f64;
                if cumulative_rank >= random_value {
                    selected.push(individual.clone());
                    break;
                }
            }
        }

        Ok(selected)
    }

    /// Crossover operation between two parents
    fn crossover(
        &self,
        parent1: &Architecture<F>,
        parent2: &Architecture<F>,
    ) -> Result<(Architecture<F>, Architecture<F>)> {
        let mut rng = scirs2_core::random::rngs::StdRng::seed_from_u64(42);
        let max_len = parent1.layers.len().min(parent2.layers.len());
        let crossover_point = if max_len > 0 {
            scirs2_core::random::Rng::random_range(&mut rng, 0..max_len)
        } else {
            0
        };

        let mut child1 = parent1.clone();
        let mut child2 = parent2.clone();

        // Single-point crossover on layers
        for i in crossover_point..child1.layers.len().min(child2.layers.len()) {
            let temp = child1.layers[i].clone();
            child1.layers[i] = child2.layers[i].clone();
            child2.layers[i] = temp;
        }

        // Reset fitness scores
        child1.fitness_score = F::zero();
        child2.fitness_score = F::zero();

        Ok((child1, child2))
    }

    /// Mutation operation on an individual
    fn mutate(&self, individual: &mut Architecture<F>) -> Result<()> {
        // Mutate layer parameters
        for layer in &mut individual.layers {
            for param in &mut layer.parameters {
                if scirs2_core::random::random::<f64>() < 0.1 {
                    let mutation_strength = F::from_f64(0.1).unwrap();
                    let random_factor =
                        F::from_f64(scirs2_core::random::random::<f64>() - 0.5).unwrap();
                    *param = *param + mutation_strength * random_factor;
                }
            }
        }

        // Mutate connection weights
        for connection in &mut individual.connections {
            if scirs2_core::random::random::<f64>() < 0.1 {
                let mutation_strength = F::from_f64(0.1).unwrap();
                let random_factor =
                    F::from_f64(scirs2_core::random::random::<f64>() - 0.5).unwrap();
                connection.weight = connection.weight + mutation_strength * random_factor;
            }
        }

        // Reset fitness score
        individual.fitness_score = F::zero();
        Ok(())
    }

    /// Get best individual from current population
    pub fn get_best_individual(&self) -> Option<&Architecture<F>> {
        self.population
            .iter()
            .max_by(|a, b| a.fitness_score.partial_cmp(&b.fitness_score).unwrap())
    }

    /// Get population statistics
    pub fn get_population_stats(&self) -> (F, F, F) {
        if self.population.is_empty() {
            return (F::zero(), F::zero(), F::zero());
        }

        let fitness_values: Vec<F> = self
            .population
            .iter()
            .map(|ind| ind.fitness_score)
            .collect();

        let mean = fitness_values.iter().fold(F::zero(), |acc, &x| acc + x)
            / F::from_usize(fitness_values.len()).unwrap();

        let max_fitness =
            fitness_values
                .iter()
                .fold(F::neg_infinity(), |acc, &x| if x > acc { x } else { acc });

        let min_fitness = fitness_values
            .iter()
            .fold(F::infinity(), |acc, &x| if x < acc { x } else { acc });

        (mean, max_fitness, min_fitness)
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> Architecture<F> {
    /// Create random architecture
    pub fn random() -> Self {
        let mut rng = scirs2_core::random::rngs::StdRng::seed_from_u64(42);
        let num_layers = 3 + scirs2_core::random::Rng::random_range(&mut rng, 0..5); // 3-7 layers
        let mut layers = Vec::new();

        for i in 0..num_layers {
            let layer = LayerConfig {
                layer_type: LayerType::Dense, // Simplified to Dense for now
                size: 32 + scirs2_core::random::Rng::random_range(&mut rng, 0..256), // 32-287 neurons
                activation: ActivationFunction::ReLU, // Simplified to ReLU
                parameters: vec![F::from_f64(scirs2_core::random::random::<f64>()).unwrap(); 4],
            };
            layers.push(layer);
        }

        let mut connections = Vec::new();
        // Create sequential connections
        for i in 0..num_layers - 1 {
            let connection = ConnectionConfig {
                from_layer: i,
                to_layer: i + 1,
                connection_type: ConnectionType::Feedforward,
                strength: F::from_f64(1.0).unwrap(),
                weight: F::from_f64(scirs2_core::random::random::<f64>()).unwrap(),
            };
            connections.push(connection);
        }

        Architecture {
            layers,
            connections,
            fitness_score: F::zero(),
        }
    }

    /// Calculate architecture complexity
    pub fn calculate_complexity(&self) -> F {
        let layer_complexity: usize = self.layers.iter().map(|layer| layer.size).sum();

        let connection_complexity = self.connections.len();

        F::from_usize(layer_complexity + connection_complexity).unwrap()
    }

    /// Validate architecture consistency
    pub fn validate(&self) -> bool {
        // Check that all connections reference valid layers
        for connection in &self.connections {
            if connection.from_layer >= self.layers.len()
                || connection.to_layer >= self.layers.len()
            {
                return false;
            }
        }

        // Check that layers have valid sizes
        for layer in &self.layers {
            if layer.size == 0 {
                return false;
            }
        }

        true
    }
}

impl<F: Float + Debug + Clone + FromPrimitive> FitnessEvaluator<F> {
    /// Create new fitness evaluator
    pub fn new(evaluation_function: EvaluationFunction) -> Self {
        FitnessEvaluator {
            evaluation_function,
            weights: vec![F::from_f64(1.0).unwrap(); 4], // Default weights
            normalization_strategy: NormalizationStrategy::MinMax,
        }
    }

    /// Evaluate fitness of an architecture
    pub fn evaluate(&self, architecture: &Architecture<F>) -> Result<F> {
        match self.evaluation_function {
            EvaluationFunction::Accuracy => self.evaluate_accuracy(architecture),
            EvaluationFunction::LatencyOptimized => self.evaluate_latency(architecture),
            EvaluationFunction::MemoryOptimized => self.evaluate_memory(architecture),
            EvaluationFunction::MultiObjective => self.evaluate_multi_objective(architecture),
        }
    }

    /// Accuracy-based fitness evaluation
    fn evaluate_accuracy(&self, architecture: &Architecture<F>) -> Result<F> {
        // Simplified accuracy estimation based on architecture properties
        let complexity_penalty = architecture.calculate_complexity() / F::from_f64(1000.0).unwrap();
        let base_accuracy = F::from_f64(0.8).unwrap(); // Base accuracy

        // Bonus for deep networks (up to a point)
        let depth_bonus = if architecture.layers.len() > 10 {
            F::from_f64(0.05).unwrap()
        } else {
            F::from_usize(architecture.layers.len()).unwrap() / F::from_f64(100.0).unwrap()
        };

        let fitness = base_accuracy + depth_bonus - complexity_penalty * F::from_f64(0.1).unwrap();
        Ok(fitness.max(F::zero()))
    }

    /// Latency-optimized fitness evaluation
    fn evaluate_latency(&self, architecture: &Architecture<F>) -> Result<F> {
        // Lower complexity = better latency fitness
        let complexity = architecture.calculate_complexity();
        let max_complexity = F::from_f64(10000.0).unwrap();

        let latency_fitness = (max_complexity - complexity) / max_complexity;
        Ok(latency_fitness.max(F::zero()))
    }

    /// Memory-optimized fitness evaluation  
    fn evaluate_memory(&self, architecture: &Architecture<F>) -> Result<F> {
        // Estimate memory usage based on layer sizes
        let memory_usage: F = architecture.layers.iter()
            .map(|layer| F::from_usize(layer.size * layer.size).unwrap()) // Approximate parameter count
            .fold(F::zero(), |acc, x| acc + x);

        let max_memory = F::from_f64(1000000.0).unwrap(); // 1M parameters
        let memory_fitness = (max_memory - memory_usage) / max_memory;

        Ok(memory_fitness.max(F::zero()))
    }

    /// Multi-objective fitness evaluation
    fn evaluate_multi_objective(&self, architecture: &Architecture<F>) -> Result<F> {
        let accuracy_score = self.evaluate_accuracy(architecture)?;
        let latency_score = self.evaluate_latency(architecture)?;
        let memory_score = self.evaluate_memory(architecture)?;

        // Weighted combination
        let accuracy_weight = F::from_f64(0.5).unwrap();
        let latency_weight = F::from_f64(0.3).unwrap();
        let memory_weight = F::from_f64(0.2).unwrap();

        let multi_objective_score = accuracy_score * accuracy_weight
            + latency_score * latency_weight
            + memory_score * memory_weight;

        Ok(multi_objective_score)
    }
}

impl MutationOperator {
    /// Create new mutation operator
    pub fn new(mutation_type: MutationType, probability: f64, intensity: f64) -> Self {
        MutationOperator {
            mutation_type,
            probability,
            intensity,
        }
    }

    /// Apply mutation to architecture
    pub fn apply<F: Float + Debug + Clone + FromPrimitive>(
        &self,
        architecture: &mut Architecture<F>,
    ) -> Result<()> {
        if scirs2_core::random::random::<f64>() > self.probability {
            return Ok(());
        }

        match self.mutation_type {
            MutationType::ParameterMutation => self.mutate_parameters(architecture),
            MutationType::StructuralMutation => self.mutate_structure(architecture),
            MutationType::LayerAddition => self.add_layer(architecture),
            MutationType::LayerRemoval => self.remove_layer(architecture),
            MutationType::ConnectionMutation => self.mutate_connections(architecture),
        }
    }

    /// Mutate layer parameters
    fn mutate_parameters<F: Float + Debug + Clone + FromPrimitive>(
        &self,
        architecture: &mut Architecture<F>,
    ) -> Result<()> {
        for layer in &mut architecture.layers {
            for param in &mut layer.parameters {
                if scirs2_core::random::random::<f64>() < 0.1 {
                    let mutation =
                        F::from_f64(self.intensity * (scirs2_core::random::random::<f64>() - 0.5))
                            .unwrap();
                    *param = *param + mutation;
                }
            }
        }
        Ok(())
    }

    /// Mutate architecture structure
    fn mutate_structure<F: Float + Debug + Clone>(
        &self,
        architecture: &mut Architecture<F>,
    ) -> Result<()> {
        // Placeholder for structural mutations
        Ok(())
    }

    /// Add new layer to architecture
    fn add_layer<F: Float + Debug + Clone + FromPrimitive>(
        &self,
        architecture: &mut Architecture<F>,
    ) -> Result<()> {
        let mut rng = scirs2_core::random::rngs::StdRng::seed_from_u64(42);
        let new_layer = LayerConfig {
            layer_type: LayerType::Dense,
            size: 32 + scirs2_core::random::Rng::random_range(&mut rng, 0..128),
            activation: ActivationFunction::ReLU,
            parameters: vec![F::from_f64(scirs2_core::random::random::<f64>()).unwrap(); 4],
        };

        architecture.layers.push(new_layer);
        Ok(())
    }

    /// Remove layer from architecture
    fn remove_layer<F: Float + Debug + Clone>(
        &self,
        architecture: &mut Architecture<F>,
    ) -> Result<()> {
        if architecture.layers.len() > 2 {
            // Keep at least 2 layers
            let mut rng = scirs2_core::random::rngs::StdRng::seed_from_u64(42);
            let remove_idx =
                scirs2_core::random::Rng::random_range(&mut rng, 0..architecture.layers.len());
            architecture.layers.remove(remove_idx);
        }
        Ok(())
    }

    /// Mutate connections
    fn mutate_connections<F: Float + Debug + Clone + FromPrimitive>(
        &self,
        architecture: &mut Architecture<F>,
    ) -> Result<()> {
        for connection in &mut architecture.connections {
            if scirs2_core::random::random::<f64>() < 0.1 {
                let mutation =
                    F::from_f64(self.intensity * (scirs2_core::random::random::<f64>() - 0.5))
                        .unwrap();
                connection.weight = connection.weight + mutation;
            }
        }
        Ok(())
    }
}

impl CrossoverOperator {
    /// Create new crossover operator
    pub fn new(crossover_type: CrossoverType, probability: f64) -> Self {
        CrossoverOperator {
            crossover_type,
            probability,
        }
    }

    /// Apply crossover between two architectures
    pub fn apply<F: Float + Debug + Clone>(
        &self,
        parent1: &Architecture<F>,
        parent2: &Architecture<F>,
    ) -> Result<(Architecture<F>, Architecture<F>)> {
        if scirs2_core::random::random::<f64>() > self.probability {
            return Ok((parent1.clone(), parent2.clone()));
        }

        match self.crossover_type {
            CrossoverType::SinglePoint => self.single_point_crossover(parent1, parent2),
            CrossoverType::TwoPoint => self.two_point_crossover(parent1, parent2),
            CrossoverType::Uniform => self.uniform_crossover(parent1, parent2),
            CrossoverType::Semantic => self.semantic_crossover(parent1, parent2),
        }
    }

    /// Single point crossover
    fn single_point_crossover<F: Float + Debug + Clone>(
        &self,
        parent1: &Architecture<F>,
        parent2: &Architecture<F>,
    ) -> Result<(Architecture<F>, Architecture<F>)> {
        let mut rng = scirs2_core::random::rngs::StdRng::seed_from_u64(42);
        let max_len = parent1.layers.len().min(parent2.layers.len());
        let crossover_point = if max_len > 0 {
            scirs2_core::random::Rng::random_range(&mut rng, 0..max_len)
        } else {
            0
        };

        let mut child1 = parent1.clone();
        let mut child2 = parent2.clone();

        // Swap layers after crossover point
        for i in crossover_point..child1.layers.len().min(child2.layers.len()) {
            let temp = child1.layers[i].clone();
            child1.layers[i] = child2.layers[i].clone();
            child2.layers[i] = temp;
        }

        Ok((child1, child2))
    }

    /// Two point crossover
    fn two_point_crossover<F: Float + Debug + Clone>(
        &self,
        parent1: &Architecture<F>,
        parent2: &Architecture<F>,
    ) -> Result<(Architecture<F>, Architecture<F>)> {
        let len = parent1.layers.len().min(parent2.layers.len());
        if len < 2 {
            return Ok((parent1.clone(), parent2.clone()));
        }

        let mut rng = scirs2_core::random::rngs::StdRng::seed_from_u64(42);
        let point1 = scirs2_core::random::Rng::random_range(&mut rng, 0..len);
        let point2 = scirs2_core::random::Rng::random_range(&mut rng, 0..len);
        let (start, end) = if point1 < point2 {
            (point1, point2)
        } else {
            (point2, point1)
        };

        let mut child1 = parent1.clone();
        let mut child2 = parent2.clone();

        // Swap layers between crossover points
        for i in start..end {
            let temp = child1.layers[i].clone();
            child1.layers[i] = child2.layers[i].clone();
            child2.layers[i] = temp;
        }

        Ok((child1, child2))
    }

    /// Uniform crossover
    fn uniform_crossover<F: Float + Debug + Clone>(
        &self,
        parent1: &Architecture<F>,
        parent2: &Architecture<F>,
    ) -> Result<(Architecture<F>, Architecture<F>)> {
        let mut child1 = parent1.clone();
        let mut child2 = parent2.clone();

        let len = child1.layers.len().min(child2.layers.len());

        // For each layer, randomly choose which parent to inherit from
        for i in 0..len {
            if scirs2_core::random::random::<bool>() {
                let temp = child1.layers[i].clone();
                child1.layers[i] = child2.layers[i].clone();
                child2.layers[i] = temp;
            }
        }

        Ok((child1, child2))
    }

    /// Semantic crossover (simplified)
    fn semantic_crossover<F: Float + Debug + Clone>(
        &self,
        parent1: &Architecture<F>,
        parent2: &Architecture<F>,
    ) -> Result<(Architecture<F>, Architecture<F>)> {
        // For now, implement as single-point crossover
        self.single_point_crossover(parent1, parent2)
    }
}