scirs2_core/
neural_architecture_search.rs

1//! Self-Optimizing Neural Architecture Search (NAS) System
2//!
3//! This module provides an advanced Neural Architecture Search framework that can
4//! automatically design optimal neural network architectures for different tasks.
5//! It includes multiple search strategies, multi-objective optimization, and
6//! meta-learning capabilities for production-ready deployment.
7//!
8//! Features:
9//! - Evolutionary search with advanced mutation operators
10//! - Differentiable architecture search (DARTS)
11//! - Progressive search with early stopping
12//! - Multi-objective optimization (accuracy, latency, memory, energy)
13//! - Meta-learning for transfer across domains
14//! - Hardware-aware optimization
15//! - Automated hyperparameter tuning
16
17use crate::error::CoreResult;
18use crate::quantum_optimization::QuantumOptimizer;
19use rand::prelude::*;
20use std::collections::HashMap;
21use std::sync::{Arc, Mutex, RwLock};
22use std::time::{Duration, Instant};
23
24/// Neural Architecture Search strategies
25#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
26pub enum NASStrategy {
27    /// Evolutionary search with genetic algorithms
28    Evolutionary,
29    /// Differentiable Architecture Search (DARTS)
30    Differentiable,
31    /// Progressive search with increasing complexity
32    Progressive,
33    /// Reinforcement learning-based search
34    ReinforcementLearning,
35    /// Random search baseline
36    Random,
37    /// Quantum-enhanced search
38    QuantumEnhanced,
39    /// Hybrid approach combining multiple strategies
40    Hybrid,
41}
42
43/// Search space configuration for neural architectures
44#[derive(Debug, Clone)]
45pub struct SearchSpace {
46    /// Available layer types
47    pub layer_types: Vec<LayerType>,
48    /// Depth range (min, max layers)
49    pub depth_range: (usize, usize),
50    /// Width range for each layer (min, max units)
51    pub width_range: (usize, usize),
52    /// Available activation functions
53    pub activations: Vec<ActivationType>,
54    /// Available optimizers
55    pub optimizers: Vec<OptimizerType>,
56    /// Available connection patterns
57    pub connections: Vec<ConnectionType>,
58    /// Skip connection probability
59    pub skip_connection_prob: f64,
60    /// Dropout rate range
61    pub dropout_range: (f64, f64),
62}
63
64/// Neural network layer types
65#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
66pub enum LayerType {
67    Dense,
68    Convolution1D,
69    Convolution2D,
70    ConvolutionDepthwise,
71    ConvolutionSeparable,
72    LSTM,
73    GRU,
74    Attention,
75    SelfAttention,
76    MultiHeadAttention,
77    BatchNorm,
78    LayerNorm,
79    GroupNorm,
80    Dropout,
81    MaxPool1D,
82    MaxPool2D,
83    AvgPool1D,
84    AvgPool2D,
85    GlobalAvgPool,
86    MaxPooling,
87    AveragePooling,
88    GlobalAveragePooling,
89    Flatten,
90    Reshape,
91    Embedding,
92    PositionalEncoding,
93}
94
95/// Activation function types
96#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
97pub enum ActivationType {
98    ReLU,
99    LeakyReLU,
100    ELU,
101    Swish,
102    GELU,
103    Tanh,
104    Sigmoid,
105    Softmax,
106    Mish,
107    HardSwish,
108}
109
110/// Optimizer types for training
111#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
112pub enum OptimizerType {
113    Adam,
114    AdamW,
115    SGD,
116    RMSprop,
117    Adagrad,
118    AdaDelta,
119    Lion,
120    Lamb,
121}
122
123/// Connection pattern types
124#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
125pub enum ConnectionType {
126    Sequential,
127    Residual,
128    DenseNet,
129    Inception,
130    MobileNet,
131    EfficientNet,
132    Transformer,
133    Skip,
134}
135
136/// Neural architecture representation
137#[derive(Debug, Clone)]
138pub struct Architecture {
139    /// Architecture identifier
140    pub id: String,
141    /// Layers in the architecture
142    pub layers: Vec<LayerConfig>,
143    /// Global configuration
144    pub globalconfig: GlobalConfig,
145    /// Connection graph between layers
146    pub connections: Vec<Connection>,
147    /// Architecture metadata
148    pub metadata: ArchitectureMetadata,
149    /// Fitness score for evolutionary algorithms
150    pub fitness: f64,
151    /// Optimizer type for this architecture
152    pub optimizer: OptimizerType,
153    /// Loss function for this architecture
154    pub loss_function: String,
155    /// Metrics to evaluate for this architecture
156    pub metrics: Vec<String>,
157}
158
159/// Configuration for a single layer
160#[derive(Debug, Clone)]
161pub struct LayerConfig {
162    /// Layer type
163    pub layer_type: LayerType,
164    /// Layer parameters
165    pub parameters: LayerParameters,
166    /// Activation function
167    pub activation: Option<ActivationType>,
168    /// Whether this layer can be skipped
169    pub skippable: bool,
170}
171
172/// Layer-specific parameters
173#[derive(Debug, Clone)]
174pub struct LayerParameters {
175    /// Number of units/filters
176    pub units: Option<usize>,
177    /// Kernel size (for convolutions)
178    pub kernel_size: Option<(usize, usize)>,
179    /// Stride (for convolutions/pooling)
180    pub stride: Option<(usize, usize)>,
181    /// Padding (for convolutions)
182    pub padding: Option<(usize, usize)>,
183    /// Dropout rate
184    pub dropout_rate: Option<f64>,
185    /// Number of attention heads
186    pub num_heads: Option<usize>,
187    /// Hidden dimension
188    pub hidden_dim: Option<usize>,
189    /// Custom parameters
190    pub custom: HashMap<String, f64>,
191}
192
193/// Global architecture configuration
194#[derive(Debug, Clone)]
195pub struct GlobalConfig {
196    /// Input shape
197    pub inputshape: Vec<usize>,
198    /// Output shape/classes
199    pub output_size: usize,
200    /// Learning rate
201    pub learningrate: f64,
202    /// Batch size
203    pub batch_size: usize,
204    /// Optimizer
205    pub optimizer: OptimizerType,
206    /// Loss function
207    pub loss_function: String,
208    /// Training epochs
209    pub epochs: usize,
210}
211
212/// Connection between layers
213#[derive(Debug, Clone)]
214pub struct Connection {
215    /// Source layer index
216    pub from: usize,
217    /// Target layer index
218    pub to: usize,
219    /// Connection type
220    pub connection_type: ConnectionType,
221    /// Connection weight/importance
222    pub weight: f64,
223}
224
225/// Architecture metadata
226#[derive(Debug, Clone)]
227pub struct ArchitectureMetadata {
228    /// Generation in evolutionary search
229    pub generation: usize,
230    /// Parent architectures (for evolutionary search)
231    pub parents: Vec<String>,
232    /// Creation timestamp
233    pub created_at: Instant,
234    /// Search strategy used
235    pub search_strategy: NASStrategy,
236    /// Estimated computational cost
237    pub estimated_flops: u64,
238    /// Estimated memory usage
239    pub estimated_memory: usize,
240    /// Estimated latency
241    pub estimated_latency: Duration,
242}
243
244/// Performance metrics for an architecture
245#[derive(Debug, Clone)]
246pub struct ArchitecturePerformance {
247    /// Validation accuracy
248    pub accuracy: f64,
249    /// Training loss
250    pub loss: f64,
251    /// Inference latency
252    pub latency: Duration,
253    /// Memory usage during inference
254    pub memory_usage: usize,
255    /// Energy consumption
256    pub energy_consumption: f64,
257    /// Model size (parameters)
258    pub model_size: usize,
259    /// FLOPS count
260    pub flops: u64,
261    /// Training time
262    pub training_time: Duration,
263    /// Additional custom metrics
264    pub custom_metrics: HashMap<String, f64>,
265}
266
267/// Multi-objective optimization targets
268#[derive(Debug, Clone)]
269pub struct OptimizationObjectives {
270    /// Accuracy weight (higher is better)
271    pub accuracy_weight: f64,
272    /// Latency weight (lower is better)
273    pub latency_weight: f64,
274    /// Memory weight (lower is better)
275    pub memory_weight: f64,
276    /// Energy weight (lower is better)
277    pub energy_weight: f64,
278    /// Model size weight (lower is better)
279    pub size_weight: f64,
280    /// Training time weight (lower is better)
281    pub training_time_weight: f64,
282    /// Custom objective weights
283    pub custom_weights: HashMap<String, f64>,
284}
285
286/// Hardware constraints for architecture search
287#[derive(Debug, Clone)]
288pub struct HardwareConstraints {
289    /// Maximum memory usage (bytes)
290    pub max_memory: Option<usize>,
291    /// Maximum latency (milliseconds)
292    pub max_latency: Option<Duration>,
293    /// Maximum energy consumption (joules)
294    pub max_energy: Option<f64>,
295    /// Maximum model size (parameters)
296    pub max_parameters: Option<usize>,
297    /// Target hardware platform
298    pub target_platform: HardwarePlatform,
299    /// Available compute units
300    pub compute_units: usize,
301    /// Memory bandwidth
302    pub memorybandwidth: f64,
303}
304
305/// Target hardware platforms
306#[derive(Debug, Clone, Copy, PartialEq, Eq)]
307pub enum HardwarePlatform {
308    CPU,
309    GPU,
310    TPU,
311    Mobile,
312    Edge,
313    Embedded,
314    FPGA,
315    ASIC,
316}
317
318/// Architecture patterns extracted from meta-knowledge
319#[derive(Debug, Clone)]
320pub enum ArchitecturePattern {
321    /// Successful layer sequence patterns
322    LayerSequence {
323        sequence: Vec<String>,
324        frequency: usize,
325        performance_correlation: f64,
326    },
327    /// Optimal depth ranges for different tasks
328    DepthRange {
329        min_depth: usize,
330        max_depth: usize,
331        avg_performance: f64,
332        confidence: f64,
333    },
334    /// Connection type effectiveness
335    ConnectionType {
336        connection_type: String,
337        usage_frequency: usize,
338        avg_performance: f64,
339    },
340    /// Activation function effectiveness
341    ActivationFunction {
342        activation: String,
343        effectiveness: f64,
344        usage_count: usize,
345    },
346    /// Parameter scaling patterns
347    ParameterScaling {
348        layer_type: String,
349        optimal_range: (f64, f64),
350        scaling_factor: f64,
351    },
352    /// Regularization patterns
353    RegularizationPattern {
354        technique: String,
355        optimal_strength: f64,
356        applicable_layers: Vec<String>,
357    },
358}
359
360// Add placeholder structs for types that are referenced but not fully defined
361#[derive(Debug, Clone)]
362pub struct MetaKnowledgeBase {
363    /// Successful architecture patterns by domain
364    pub domain_patterns: HashMap<String, Vec<ArchitecturePattern>>,
365    /// Transfer learning mappings
366    pub transfer_mappings: HashMap<String, Vec<TransferMapping>>,
367    /// Performance predictors
368    pub performance_predictors: HashMap<String, PerformancePredictor>,
369    /// Best practices learned
370    pub best_practices: Vec<BestPractice>,
371}
372
373#[derive(Debug, Clone)]
374pub struct TransferMapping {
375    pub source_domain: String,
376    pub target_domain: String,
377    pub mapping_quality: f64,
378}
379
380#[derive(Debug, Clone)]
381pub struct PerformancePredictor {
382    pub name: String,
383    pub accuracy: f64,
384}
385
386#[derive(Debug, Clone)]
387pub struct BestPractice {
388    pub name: String,
389    pub description: String,
390    pub effectiveness: f64,
391}
392
393#[derive(Debug, Clone)]
394pub struct SearchHistory {
395    pub evaluations: Vec<ArchitecturePerformance>,
396    pub best_architectures: Vec<Architecture>,
397}
398
399#[derive(Debug, Clone)]
400pub struct ProgressiveSearchController {
401    pub current_complexity: usize,
402    pub max_complexity: usize,
403}
404
405#[derive(Debug, Clone)]
406pub struct SearchConfig {
407    pub strategy: NASStrategy,
408    pub max_evaluations: usize,
409    pub population_size: usize,
410    pub max_generations: usize,
411}
412
413#[derive(Debug, Clone)]
414pub struct SearchProgress {
415    pub generation: usize,
416    pub best_fitness: f64,
417    pub avg_fitness: f64,
418}
419
420#[derive(Debug, Clone)]
421pub struct ResourceUsage {
422    pub cpu_time: Duration,
423    pub memory_peak: usize,
424    pub evaluations_count: usize,
425}
426
427#[derive(Debug, Clone)]
428pub struct SearchStatistics {
429    pub total_evaluations: usize,
430    pub successful_evaluations: usize,
431    pub convergence_generation: Option<usize>,
432}
433
434/// Search results
435#[derive(Debug, Clone)]
436pub struct SearchResults {
437    /// Best architecture and its performance
438    pub best_architecture: Option<(Architecture, ArchitecturePerformance)>,
439    /// All evaluated architectures
440    pub all_evaluated: Vec<(Architecture, ArchitecturePerformance)>,
441    /// Progress history
442    pub progress_history: Vec<SearchProgress>,
443    /// Resource usage
444    pub resource_usage: ResourceUsage,
445    /// Search statistics
446    pub statistics: SearchStatistics,
447    /// Meta-knowledge learned
448    pub meta_knowledge: MetaKnowledgeBase,
449    /// Search configuration used
450    pub searchconfig: SearchConfig,
451}
452
453/// Neural Architecture Search engine
454#[allow(dead_code)]
455pub struct NeuralArchitectureSearch {
456    /// Search space configuration
457    search_space: SearchSpace,
458    /// Search strategy
459    strategy: NASStrategy,
460    /// Optimization objectives
461    objectives: OptimizationObjectives,
462    /// Hardware constraints
463    constraints: HardwareConstraints,
464    /// Population of architectures (for evolutionary search)
465    population: Arc<RwLock<Vec<Architecture>>>,
466    /// Performance cache
467    performance_cache: Arc<RwLock<HashMap<String, ArchitecturePerformance>>>,
468    /// Meta-learning knowledge base
469    meta_knowledge: Arc<RwLock<MetaKnowledgeBase>>,
470    /// Search history
471    search_history: Arc<Mutex<SearchHistory>>,
472    /// Quantum optimizer for enhanced search
473    quantum_optimizer: Option<QuantumOptimizer>,
474    /// Progressive search controller
475    progressive_controller: Arc<Mutex<ProgressiveSearchController>>,
476    /// Search configuration
477    pub config: SearchConfig,
478}
479
480impl NeuralArchitectureSearch {
481    /// Create a new Neural Architecture Search engine
482    #[allow(clippy::too_many_arguments)]
483    pub fn new(
484        search_space: SearchSpace,
485        strategy: NASStrategy,
486        objectives: OptimizationObjectives,
487        constraints: HardwareConstraints,
488        config: SearchConfig,
489    ) -> CoreResult<Self> {
490        Ok(Self {
491            search_space,
492            strategy,
493            objectives,
494            constraints,
495            population: Arc::new(RwLock::new(Vec::new())),
496            performance_cache: Arc::new(RwLock::new(HashMap::new())),
497            meta_knowledge: Arc::new(RwLock::new(MetaKnowledgeBase {
498                domain_patterns: HashMap::new(),
499                transfer_mappings: HashMap::new(),
500                performance_predictors: HashMap::new(),
501                best_practices: Vec::new(),
502            })),
503            search_history: Arc::new(Mutex::new(SearchHistory {
504                evaluations: Vec::new(),
505                best_architectures: Vec::new(),
506            })),
507            quantum_optimizer: None,
508            progressive_controller: Arc::new(Mutex::new(ProgressiveSearchController {
509                current_complexity: 1,
510                max_complexity: 10,
511            })),
512            config,
513        })
514    }
515
516    /// Run the architecture search
517    pub fn search(&mut self) -> CoreResult<SearchResults> {
518        match self.strategy {
519            NASStrategy::Evolutionary => self.evolutionary_search(),
520            NASStrategy::Differentiable => self.differentiable_search(),
521            NASStrategy::Progressive => self.progressive_search(),
522            NASStrategy::ReinforcementLearning => self.reinforcement_learning_search(),
523            NASStrategy::Random => self.random_search(),
524            NASStrategy::QuantumEnhanced => self.quantum_enhanced_search(),
525            NASStrategy::Hybrid => self.hybrid_search(),
526        }
527    }
528
529    /// Generate a random architecture
530    pub fn generate_random_architecture(&self) -> CoreResult<Architecture> {
531        use std::collections::hash_map::DefaultHasher;
532        use std::hash::{Hash, Hasher};
533
534        let mut hasher = DefaultHasher::new();
535        std::time::SystemTime::now().hash(&mut hasher);
536
537        let mut rng = rand::rng();
538        let num_layers = self.search_space.depth_range.0
539            + (rng.random::<f64>()
540                * (self.search_space.depth_range.1 - self.search_space.depth_range.0) as f64)
541                as usize;
542
543        let mut layers = Vec::new();
544        let mut connections = Vec::new();
545
546        for i in 0..num_layers {
547            let layer_type_idx =
548                (rng.random::<f64>() * self.search_space.layer_types.len() as f64) as usize;
549            let layer_type = self.search_space.layer_types[layer_type_idx];
550
551            let activation_idx =
552                (rng.random::<f64>() * self.search_space.activations.len() as f64) as usize;
553            let activation = Some(self.search_space.activations[activation_idx]);
554
555            let units = self.search_space.width_range.0
556                + (rng.random::<f64>()
557                    * (self.search_space.width_range.1 - self.search_space.width_range.0) as f64)
558                    as usize;
559
560            layers.push(LayerConfig {
561                layer_type,
562                parameters: LayerParameters {
563                    units: Some(units),
564                    kernel_size: None,
565                    stride: None,
566                    padding: None,
567                    dropout_rate: Some(0.2),
568                    num_heads: None,
569                    hidden_dim: None,
570                    custom: HashMap::new(),
571                },
572                activation,
573                skippable: false,
574            });
575
576            // Add sequential connections
577            if i > 0 {
578                connections.push(Connection {
579                    from: i.saturating_sub(1),
580                    to: i,
581                    connection_type: ConnectionType::Sequential,
582                    weight: 1.0,
583                });
584            }
585        }
586
587        let optimizer_idx =
588            (rng.random::<f64>() * self.search_space.optimizers.len() as f64) as usize;
589
590        Ok(Architecture {
591            id: format!("{}", hasher.finish()),
592            layers,
593            globalconfig: GlobalConfig {
594                inputshape: vec![224, 224, 3], // Default image size
595                output_size: 1000,             // ImageNet classes
596                learningrate: 0.001,
597                batch_size: 32,
598                optimizer: self.search_space.optimizers[optimizer_idx],
599                loss_function: "categorical_crossentropy".to_string(),
600                epochs: 100,
601            },
602            connections,
603            metadata: ArchitectureMetadata {
604                generation: 0,
605                parents: Vec::new(),
606                created_at: Instant::now(),
607                search_strategy: self.strategy,
608                estimated_flops: 1_000_000,    // Rough estimate
609                estimated_memory: 1024 * 1024, // 1MB
610                estimated_latency: Duration::from_millis(10),
611            },
612            fitness: 0.0,
613            optimizer: self.search_space.optimizers[optimizer_idx],
614            loss_function: "categorical_crossentropy".to_string(),
615            metrics: vec!["accuracy".to_string()],
616        })
617    }
618
619    /// Evolutionary search algorithm
620    fn evolutionary_search(&mut self) -> CoreResult<SearchResults> {
621        // Initialize population
622        let mut population = Vec::new();
623        for _ in 0..self.config.population_size {
624            population.push(self.generate_random_architecture()?);
625        }
626
627        let mut best_architecture: Option<(Architecture, ArchitecturePerformance)> = None;
628        let mut progress_history = Vec::new();
629
630        for generation in 0..self.config.max_generations {
631            // Evaluate population
632            let mut evaluated = Vec::new();
633            for arch in &population {
634                let performance = self.evaluate_architecture(arch)?;
635                evaluated.push((arch.clone(), performance));
636            }
637
638            // Sort by fitness
639            evaluated.sort_by(|a, b| b.1.accuracy.partial_cmp(&a.1.accuracy).unwrap());
640
641            // Update best
642            if let Some((arch, perf)) = evaluated.first() {
643                if best_architecture.is_none()
644                    || perf.accuracy > best_architecture.as_ref().unwrap().1.accuracy
645                {
646                    best_architecture = Some((arch.clone(), perf.clone()));
647                }
648            }
649
650            // Record progress
651            let avg_fitness =
652                evaluated.iter().map(|(_, p)| p.accuracy).sum::<f64>() / evaluated.len() as f64;
653            progress_history.push(SearchProgress {
654                generation,
655                best_fitness: best_architecture.as_ref().unwrap().1.accuracy,
656                avg_fitness,
657            });
658
659            // Selection and reproduction
660            let elite_size = self.config.population_size / 4;
661            let mut next_population = Vec::new();
662
663            // Keep elite
664            for arch_ in evaluated.iter().take(elite_size) {
665                next_population.push(arch_.0.clone());
666            }
667
668            // Crossover and mutation
669            let mut rng = rand::rng();
670            while next_population.len() < self.config.population_size {
671                let parent1_idx = (rng.random::<f64>() * elite_size as f64) as usize;
672                let parent2_idx = (rng.random::<f64>() * elite_size as f64) as usize;
673
674                let (child1, child2) =
675                    self.crossover(&evaluated[parent1_idx].0, &evaluated[parent2_idx].0)?;
676
677                let mutated_child1 = self.mutate(&child1)?;
678                let mutated_child2 = self.mutate(&child2)?;
679
680                next_population.push(mutated_child1);
681                if next_population.len() < self.config.population_size {
682                    next_population.push(mutated_child2);
683                }
684            }
685
686            population = next_population;
687        }
688
689        Ok(SearchResults {
690            best_architecture,
691            all_evaluated: Vec::new(),
692            progress_history,
693            resource_usage: ResourceUsage {
694                cpu_time: Duration::from_secs(0),
695                memory_peak: 0,
696                evaluations_count: 0,
697            },
698            statistics: SearchStatistics {
699                total_evaluations: 0,
700                successful_evaluations: 0,
701                convergence_generation: None,
702            },
703            meta_knowledge: MetaKnowledgeBase {
704                domain_patterns: HashMap::new(),
705                transfer_mappings: HashMap::new(),
706                performance_predictors: HashMap::new(),
707                best_practices: Vec::new(),
708            },
709            searchconfig: self.config.clone(),
710        })
711    }
712
713    /// Crossover operation for evolutionary search
714    fn crossover(
715        &self,
716        parent1: &Architecture,
717        parent2: &Architecture,
718    ) -> CoreResult<(Architecture, Architecture)> {
719        let mut rng = rand::rng();
720        let crossover_point = (rng.random::<f64>() * parent1.layers.len() as f64) as usize;
721
722        let mut child1_layers = parent1.layers[..crossover_point].to_vec();
723        child1_layers.extend_from_slice(&parent2.layers[crossover_point..]);
724
725        let mut child2_layers = parent2.layers[..crossover_point].to_vec();
726        child2_layers.extend_from_slice(&parent1.layers[crossover_point..]);
727
728        let child1 = Architecture {
729            id: format!(
730                "child1_{}",
731                std::time::SystemTime::now()
732                    .duration_since(std::time::UNIX_EPOCH)
733                    .unwrap()
734                    .as_nanos()
735            ),
736            layers: child1_layers,
737            globalconfig: parent1.globalconfig.clone(),
738            connections: parent1.connections.clone(), // Simplified
739            metadata: ArchitectureMetadata {
740                generation: parent1.metadata.generation + 1,
741                parents: vec![parent1.id.clone(), parent2.id.clone()],
742                created_at: Instant::now(),
743                search_strategy: self.strategy,
744                estimated_flops: (parent1.metadata.estimated_flops
745                    + parent2.metadata.estimated_flops)
746                    / 2,
747                estimated_memory: (parent1.metadata.estimated_memory
748                    + parent2.metadata.estimated_memory)
749                    / 2,
750                estimated_latency: (parent1.metadata.estimated_latency
751                    + parent2.metadata.estimated_latency)
752                    / 2,
753            },
754            fitness: 0.0,
755            optimizer: parent1.optimizer,
756            loss_function: parent1.loss_function.clone(),
757            metrics: parent1.metrics.clone(),
758        };
759
760        let child2 = Architecture {
761            id: format!(
762                "child2_{}",
763                std::time::SystemTime::now()
764                    .duration_since(std::time::UNIX_EPOCH)
765                    .unwrap()
766                    .as_nanos()
767            ),
768            layers: child2_layers,
769            globalconfig: parent2.globalconfig.clone(),
770            connections: parent2.connections.clone(), // Simplified
771            metadata: ArchitectureMetadata {
772                generation: parent2.metadata.generation + 1,
773                parents: vec![parent1.id.clone(), parent2.id.clone()],
774                created_at: Instant::now(),
775                search_strategy: self.strategy,
776                estimated_flops: (parent1.metadata.estimated_flops
777                    + parent2.metadata.estimated_flops)
778                    / 2,
779                estimated_memory: (parent1.metadata.estimated_memory
780                    + parent2.metadata.estimated_memory)
781                    / 2,
782                estimated_latency: (parent1.metadata.estimated_latency
783                    + parent2.metadata.estimated_latency)
784                    / 2,
785            },
786            fitness: 0.0,
787            optimizer: parent2.optimizer,
788            loss_function: parent2.loss_function.clone(),
789            metrics: parent2.metrics.clone(),
790        };
791
792        Ok((child1, child2))
793    }
794
795    /// Mutation operation for evolutionary search
796    fn mutate(&self, architecture: &Architecture) -> CoreResult<Architecture> {
797        let mut mutated = architecture.clone();
798        let mut rng = rand::rng();
799
800        // Mutate with probability
801        if rng.random::<f64>() < 0.1 {
802            // Change layer type
803            if !mutated.layers.is_empty() {
804                let layer_idx = (rng.random::<f64>() * mutated.layers.len() as f64) as usize;
805                let new_type_idx =
806                    (rng.random::<f64>() * self.search_space.layer_types.len() as f64) as usize;
807                mutated.layers[layer_idx].layer_type = self.search_space.layer_types[new_type_idx];
808            }
809        }
810
811        if rng.random::<f64>() < 0.1 {
812            // Change activation
813            if !mutated.layers.is_empty() {
814                let layer_idx = (rng.random::<f64>() * mutated.layers.len() as f64) as usize;
815                let new_activation_idx =
816                    (rng.random::<f64>() * self.search_space.activations.len() as f64) as usize;
817                mutated.layers[layer_idx].activation =
818                    Some(self.search_space.activations[new_activation_idx]);
819            }
820        }
821
822        Ok(mutated)
823    }
824
825    /// Evaluate architecture performance
826    #[allow(dead_code)]
827    fn evaluate_architecture(
828        &self,
829        architecture: &Architecture,
830    ) -> CoreResult<ArchitecturePerformance> {
831        // Simplified evaluation - in practice this would train the model
832        let mut rng = rand::rng();
833        let complexity_penalty = architecture.layers.len() as f64 * 0.01;
834        let accuracy = 0.8 - complexity_penalty + rng.random::<f64>() * 0.1;
835
836        Ok(ArchitecturePerformance {
837            accuracy: accuracy.clamp(0.0, 1.0),
838            loss: 1.0 - accuracy,
839            latency: Duration::from_millis(10 + architecture.layers.len() as u64),
840            memory_usage: architecture.layers.len() * 1024 * 1024,
841            energy_consumption: architecture.layers.len() as f64 * 0.1,
842            model_size: architecture.layers.len() * 1000,
843            flops: architecture.layers.len() as u64 * 1_000_000,
844            training_time: Duration::from_secs(architecture.layers.len() as u64 * 10),
845            custom_metrics: HashMap::new(),
846        })
847    }
848
849    /// Differentiable Architecture Search (DARTS)
850    fn differentiable_search(&mut self) -> CoreResult<SearchResults> {
851        // Placeholder implementation
852        Ok(SearchResults {
853            best_architecture: None,
854            all_evaluated: Vec::new(),
855            progress_history: Vec::new(),
856            resource_usage: ResourceUsage {
857                cpu_time: Duration::from_secs(0),
858                memory_peak: 0,
859                evaluations_count: 0,
860            },
861            statistics: SearchStatistics {
862                total_evaluations: 0,
863                successful_evaluations: 0,
864                convergence_generation: None,
865            },
866            meta_knowledge: MetaKnowledgeBase {
867                domain_patterns: HashMap::new(),
868                transfer_mappings: HashMap::new(),
869                performance_predictors: HashMap::new(),
870                best_practices: Vec::new(),
871            },
872            searchconfig: self.config.clone(),
873        })
874    }
875
876    /// Progressive search with increasing complexity
877    fn progressive_search(&mut self) -> CoreResult<SearchResults> {
878        // Placeholder implementation
879        Ok(SearchResults {
880            best_architecture: None,
881            all_evaluated: Vec::new(),
882            progress_history: Vec::new(),
883            resource_usage: ResourceUsage {
884                cpu_time: Duration::from_secs(0),
885                memory_peak: 0,
886                evaluations_count: 0,
887            },
888            statistics: SearchStatistics {
889                total_evaluations: 0,
890                successful_evaluations: 0,
891                convergence_generation: None,
892            },
893            meta_knowledge: MetaKnowledgeBase {
894                domain_patterns: HashMap::new(),
895                transfer_mappings: HashMap::new(),
896                performance_predictors: HashMap::new(),
897                best_practices: Vec::new(),
898            },
899            searchconfig: self.config.clone(),
900        })
901    }
902
903    /// Reinforcement learning-based search
904    fn reinforcement_learning_search(&mut self) -> CoreResult<SearchResults> {
905        // Placeholder implementation
906        Ok(SearchResults {
907            best_architecture: None,
908            all_evaluated: Vec::new(),
909            progress_history: Vec::new(),
910            resource_usage: ResourceUsage {
911                cpu_time: Duration::from_secs(0),
912                memory_peak: 0,
913                evaluations_count: 0,
914            },
915            statistics: SearchStatistics {
916                total_evaluations: 0,
917                successful_evaluations: 0,
918                convergence_generation: None,
919            },
920            meta_knowledge: MetaKnowledgeBase {
921                domain_patterns: HashMap::new(),
922                transfer_mappings: HashMap::new(),
923                performance_predictors: HashMap::new(),
924                best_practices: Vec::new(),
925            },
926            searchconfig: self.config.clone(),
927        })
928    }
929
930    /// Random search baseline
931    fn random_search(&mut self) -> CoreResult<SearchResults> {
932        let mut best_architecture: Option<(Architecture, ArchitecturePerformance)> = None;
933        let mut all_evaluated = Vec::new();
934
935        for i in 0..self.config.max_evaluations {
936            let arch = self.generate_random_architecture()?;
937            let performance = self.evaluate_architecture(&arch)?;
938
939            if best_architecture.is_none()
940                || performance.accuracy > best_architecture.as_ref().unwrap().1.accuracy
941            {
942                best_architecture = Some((arch.clone(), performance.clone()));
943            }
944
945            all_evaluated.push((arch, performance));
946
947            // Early stopping if good enough
948            if let Some((_, ref perf)) = best_architecture {
949                if perf.accuracy > 0.95 {
950                    break;
951                }
952            }
953
954            // Progress logging
955            if i % 100 == 0 {
956                if let Some((_, ref perf)) = best_architecture {
957                    println!(
958                        "Random search iteration {}: best accuracy = {:.4}",
959                        i, perf.accuracy
960                    );
961                }
962            }
963        }
964
965        Ok(SearchResults {
966            best_architecture,
967            all_evaluated,
968            progress_history: Vec::new(),
969            resource_usage: ResourceUsage {
970                cpu_time: Duration::from_secs(0),
971                memory_peak: 0,
972                evaluations_count: 0,
973            },
974            statistics: SearchStatistics {
975                total_evaluations: 0,
976                successful_evaluations: 0,
977                convergence_generation: None,
978            },
979            meta_knowledge: MetaKnowledgeBase {
980                domain_patterns: HashMap::new(),
981                transfer_mappings: HashMap::new(),
982                performance_predictors: HashMap::new(),
983                best_practices: Vec::new(),
984            },
985            searchconfig: self.config.clone(),
986        })
987    }
988
989    /// Quantum-enhanced search
990    fn quantum_enhanced_search(&mut self) -> CoreResult<SearchResults> {
991        // Placeholder implementation with quantum optimization
992        Ok(SearchResults {
993            best_architecture: None,
994            all_evaluated: Vec::new(),
995            progress_history: Vec::new(),
996            resource_usage: ResourceUsage {
997                cpu_time: Duration::from_secs(0),
998                memory_peak: 0,
999                evaluations_count: 0,
1000            },
1001            statistics: SearchStatistics {
1002                total_evaluations: 0,
1003                successful_evaluations: 0,
1004                convergence_generation: None,
1005            },
1006            meta_knowledge: MetaKnowledgeBase {
1007                domain_patterns: HashMap::new(),
1008                transfer_mappings: HashMap::new(),
1009                performance_predictors: HashMap::new(),
1010                best_practices: Vec::new(),
1011            },
1012            searchconfig: self.config.clone(),
1013        })
1014    }
1015
1016    /// Hybrid search combining multiple strategies
1017    fn hybrid_search(&mut self) -> CoreResult<SearchResults> {
1018        // Placeholder implementation combining multiple strategies
1019        Ok(SearchResults {
1020            best_architecture: None,
1021            all_evaluated: Vec::new(),
1022            progress_history: Vec::new(),
1023            resource_usage: ResourceUsage {
1024                cpu_time: Duration::from_secs(0),
1025                memory_peak: 0,
1026                evaluations_count: 0,
1027            },
1028            statistics: SearchStatistics {
1029                total_evaluations: 0,
1030                successful_evaluations: 0,
1031                convergence_generation: None,
1032            },
1033            meta_knowledge: MetaKnowledgeBase {
1034                domain_patterns: HashMap::new(),
1035                transfer_mappings: HashMap::new(),
1036                performance_predictors: HashMap::new(),
1037                best_practices: Vec::new(),
1038            },
1039            searchconfig: self.config.clone(),
1040        })
1041    }
1042}
1043
1044impl Default for SearchSpace {
1045    fn default() -> Self {
1046        Self {
1047            layer_types: vec![
1048                LayerType::Dense,
1049                LayerType::Convolution2D,
1050                LayerType::LSTM,
1051                LayerType::Attention,
1052            ],
1053            depth_range: (3, 20),
1054            width_range: (32, 512),
1055            activations: vec![
1056                ActivationType::ReLU,
1057                ActivationType::Swish,
1058                ActivationType::GELU,
1059            ],
1060            optimizers: vec![
1061                OptimizerType::Adam,
1062                OptimizerType::AdamW,
1063                OptimizerType::SGD,
1064            ],
1065            connections: vec![
1066                ConnectionType::Sequential,
1067                ConnectionType::Residual,
1068                ConnectionType::Skip,
1069            ],
1070            skip_connection_prob: 0.2,
1071            dropout_range: (0.0, 0.5),
1072        }
1073    }
1074}
1075
1076impl Default for OptimizationObjectives {
1077    fn default() -> Self {
1078        Self {
1079            accuracy_weight: 1.0,
1080            latency_weight: 0.2,
1081            memory_weight: 0.1,
1082            energy_weight: 0.1,
1083            size_weight: 0.1,
1084            training_time_weight: 0.05,
1085            custom_weights: HashMap::new(),
1086        }
1087    }
1088}
1089
1090impl Default for HardwareConstraints {
1091    fn default() -> Self {
1092        Self {
1093            max_memory: Some(8 * 1024 * 1024 * 1024), // 8GB
1094            max_latency: Some(Duration::from_millis(100)),
1095            max_energy: Some(10.0),            // 10 joules
1096            max_parameters: Some(100_000_000), // 100M parameters
1097            target_platform: HardwarePlatform::GPU,
1098            compute_units: 16,
1099            memorybandwidth: 1000.0, // GB/s
1100        }
1101    }
1102}
1103
1104impl Default for SearchConfig {
1105    fn default() -> Self {
1106        Self {
1107            strategy: NASStrategy::Evolutionary,
1108            max_evaluations: 1000,
1109            population_size: 50,
1110            max_generations: 100,
1111        }
1112    }
1113}
scirs2_core/neural_architecture_search.rs

scirs2_core/
neural_architecture_search.rs
