import asyncio import inspect import hashlib from typing import Dict, List, Any, Optional, Callable import logging from dataclasses import dataclass from enum import Enum import random import numpy as np class EvolutionStrategy(Enum): """Stratégies d'évolution""" GRADIENT_BASED = "gradient_based" GENETIC_PROGRAMMING = "genetic_programming" NEURAL_ARCHITECTURE_SEARCH = "neural_architecture_search" QUANTUM_EVOLUTION = "quantum_evolution" META_LEARNING = "meta_learning" class ImprovementMetric(Enum): """Métriques d'amélioration""" PERFORMANCE = "performance" EFFICIENCY = "efficiency" ACCURACY = "accuracy" ROBUSTNESS = "robustness" ADAPTABILITY = "adaptability" @dataclass class SystemVersion: """Version du système avec métriques""" version_id: str code_components: Dict[str, Any] performance_metrics: Dict[str, float] improvement_score: float evolutionary_path: List[str] @dataclass class EvolutionStep: """Étape d'évolution du système""" step_id: str strategy: EvolutionStrategy changes: Dict[str, Any] improvement: float learning_rate: float class SelfEvolvingSystem: """ Système capable de s'auto-améliorer et d'évoluer continuellement grâce à des algorithmes d'évolution avancés """ def __init__(self): self.logger = logging.getLogger("self_evolving_system") self.current_version = None self.evolution_history: List[EvolutionStep] = [] self.performance_baseline = {} self.improvement_targets = {} self.evolution_strategies = { EvolutionStrategy.GRADIENT_BASED: self._gradient_based_evolution, EvolutionStrategy.GENETIC_PROGRAMMING: self._genetic_programming_evolution, EvolutionStrategy.NEURAL_ARCHITECTURE_SEARCH: self._neural_architecture_search, EvolutionStrategy.QUANTUM_EVOLUTION: self._quantum_evolution, EvolutionStrategy.META_LEARNING: self._meta_learning_evolution } async def initialize(self): """Initialise le système auto-évolutif""" self.logger.info("🔄 Initialisation du système auto-évolutif...") try: await self._establish_baseline_performance() await self._initialize_evolution_engine() await self._create_initial_version() self.logger.info("✅ Système auto-évolutif initialisé") return True except Exception as e: self.logger.error(f"❌ Erreur d'initialisation: {e}") return False async def evolve_system(self, target_metrics: Dict[ImprovementMetric, float], strategy: EvolutionStrategy = EvolutionStrategy.QUANTUM_EVOLUTION) -> SystemVersion: """Fait évoluer le système vers les métriques cibles""" try: self.logger.info(f"🎯 Début de l'évolution vers {target_metrics}") # Sélection de la stratégie d'évolution evolution_algorithm = self.evolution_strategies.get(strategy, self._quantum_evolution) # Processus d'évolution evolution_result = await evolution_algorithm(target_metrics) # Création de la nouvelle version new_version = await self._create_new_version(evolution_result) # Validation de l'amélioration improvement_validated = await self._validate_improvement(new_version) if improvement_validated: self.current_version = new_version self.evolution_history.append(evolution_result["evolution_step"]) self.logger.info(f"🚀 Nouvelle version créée: {new_version.version_id}") else: self.logger.warning("⚠️ L'évolution n'a pas apporté d'amélioration significative") return new_version except Exception as e: self.logger.error(f"Erreur d'évolution: {e}") raise async def continuous_self_improvement(self, improvement_interval: float = 3600) -> None: """Lance l'auto-amélioration continue en arrière-plan""" self.logger.info(f"🔄 Auto-amélioration continue activée (intervalle: {improvement_interval}s)") while True: try: # Analyse des performances actuelles current_performance = await self._analyze_current_performance() # Définition des cibles d'amélioration improvement_targets = await self._calculate_improvement_targets(current_performance) # Évolution await self.evolve_system(improvement_targets) # Pause avant la prochaine itération await asyncio.sleep(improvement_interval) except Exception as e: self.logger.error(f"Erreur dans l'auto-amélioration continue: {e}") await asyncio.sleep(60) # Pause plus courte en cas d'erreur async def optimize_for_environment(self, environment_metrics: Dict[str, Any]) -> SystemVersion: """Optimise le système pour un environnement spécifique""" try: self.logger.info(f"🌍 Optimisation pour l'environnement: {environment_metrics}") # Analyse de l'environnement environment_analysis = await self._analyze_environment(environment_metrics) # Adaptation évolutive adapted_version = await self._evolutionary_adaptation(environment_analysis) # Fine-tuning optimized_version = await self._environment_specific_tuning(adapted_version, environment_analysis) self.logger.info(f"🎯 Optimisation environnementale terminée") return optimized_version except Exception as e: self.logger.error(f"Erreur d'optimisation environnementale: {e}") raise async def transfer_learning(self, source_domain: str, target_domain: str) -> SystemVersion: """Transfert d'apprentissage entre domaines""" try: self.logger.info(f"📚 Transfert d'apprentissage de {source_domain} vers {target_domain}") # Extraction des connaissances du domaine source source_knowledge = await self._extract_domain_knowledge(source_domain) # Adaptation au domaine cible transferred_knowledge = await self._adapt_knowledge_to_domain(source_knowledge, target_domain) # Intégration des connaissances transférées enhanced_version = await self._integrate_transferred_knowledge(transferred_knowledge) self.logger.info("✅ Transfert d'apprentissage réussi") return enhanced_version except Exception as e: self.logger.error(f"Erreur de transfert d'apprentissage: {e}") raise async def meta_learning_optimization(self, learning_tasks: List[Dict[str, Any]]) -> SystemVersion: """Optimisation par méta-apprentissage""" try: self.logger.info(f"🧠 Méta-apprentissage sur {len(learning_tasks)} tâches") # Apprentissage des patterns d'apprentissage learning_patterns = await self._extract_learning_patterns(learning_tasks) # Optimisation de l'algorithme d'apprentissage optimized_learner = await self._optimize_learning_algorithm(learning_patterns) # Création de la version méta-optimisée meta_optimized_version = await self._create_meta_optimized_version(optimized_learner) self.logger.info("🎯 Méta-optimisation terminée") return meta_optimized_version except Exception as e: self.logger.error(f"Erreur de méta-optimisation: {e}") raise async def _establish_baseline_performance(self): """Établit les performances de référence""" self.performance_baseline = { "response_time": 1.0, "accuracy": 0.85, "resource_usage": 1.0, "adaptability": 0.7 } self.logger.info("📊 Performances de référence établies") async def _initialize_evolution_engine(self): """Initialise le moteur d'évolution""" self.logger.info("⚙️ Initialisation du moteur d'évolution...") # Configuration des stratégies d'évolution self.improvement_targets = { ImprovementMetric.PERFORMANCE: 0.1, # 10% d'amélioration ImprovementMetric.EFFICIENCY: 0.15, # 15% d'amélioration ImprovementMetric.ACCURACY: 0.05, # 5% d'amélioration ImprovementMetric.ROBUSTNESS: 0.2, # 20% d'amélioration ImprovementMetric.ADAPTABILITY: 0.25 # 25% d'amélioration } async def _create_initial_version(self): """Crée la version initiale du système""" self.current_version = SystemVersion( version_id="v1.0.0_initial", code_components={}, performance_metrics=self.performance_baseline.copy(), improvement_score=0.0, evolutionary_path=["initial"] ) self.logger.info("🆕 Version initiale créée") async def _gradient_based_evolution(self, target_metrics: Dict[ImprovementMetric, float]) -> Dict[str, Any]: """Évolution basée sur le gradient""" self.logger.info("📈 Évolution basée sur le gradient") # Calcul des gradients d'amélioration improvement_gradients = await self._calculate_improvement_gradients(target_metrics) # Application des mises à jour updates = await self._apply_gradient_updates(improvement_gradients) return { "strategy": EvolutionStrategy.GRADIENT_BASED, "updates": updates, "improvement": await self._estimate_improvement(updates), "evolution_step": EvolutionStep( step_id=f"gradient_{hashlib.md5(str(updates).encode()).hexdigest()[:8]}", strategy=EvolutionStrategy.GRADIENT_BASED, changes=updates, improvement=0.1, # Estimation learning_rate=0.01 ) } async def _genetic_programming_evolution(self, target_metrics: Dict[ImprovementMetric, float]) -> Dict[str, Any]: """Évolution par programmation génétique""" self.logger.info("🧬 Évolution par programmation génétique") # Génération de la population initiale population = await self._generate_genetic_population() # Évaluation de la fitness fitness_scores = await self._evaluate_genetic_fitness(population, target_metrics) # Sélection et reproduction new_generation = await self._genetic_selection_and_reproduction(population, fitness_scores) # Mutation mutated_generation = await self._apply_genetic_mutations(new_generation) return { "strategy": EvolutionStrategy.GENETIC_PROGRAMMING, "best_solution": await self._extract_best_solution(mutated_generation, fitness_scores), "improvement": await self._estimate_genetic_improvement(mutated_generation), "evolution_step": EvolutionStep( step_id=f"genetic_{hashlib.md5(str(mutated_generation).encode()).hexdigest()[:8]}", strategy=EvolutionStrategy.GENETIC_PROGRAMMING, changes={"generation": mutated_generation}, improvement=0.15, # Estimation learning_rate=0.02 ) } async def _neural_architecture_search(self, target_metrics: Dict[ImprovementMetric, float]) -> Dict[str, Any]: """Recherche d'architecture neuronale""" self.logger.info("🧠 Recherche d'architecture neuronale") # Exploration de l'espace d'architectures architecture_space = await self._define_architecture_space() # Évaluation des architectures candidates architecture_evaluations = await self._evaluate_architectures(architecture_space, target_metrics) # Sélection de la meilleure architecture best_architecture = await self._select_best_architecture(architecture_evaluations) return { "strategy": EvolutionStrategy.NEURAL_ARCHITECTURE_SEARCH, "best_architecture": best_architecture, "improvement": await self._estimate_architecture_improvement(best_architecture), "evolution_step": EvolutionStep( step_id=f"nas_{hashlib.md5(str(best_architecture).encode()).hexdigest()[:8]}", strategy=EvolutionStrategy.NEURAL_ARCHITECTURE_SEARCH, changes={"architecture": best_architecture}, improvement=0.2, # Estimation learning_rate=0.015 ) } async def _quantum_evolution(self, target_metrics: Dict[ImprovementMetric, float]) -> Dict[str, Any]: """Évolution quantique""" self.logger.info("⚛️ Évolution quantique") # Préparation de l'état quantique d'évolution quantum_state = await self._prepare_quantum_evolution_state(target_metrics) # Application des opérateurs quantiques d'évolution evolved_state = await self._apply_quantum_evolution_operators(quantum_state) # Mesure et extraction de la solution quantum_solution = await self._measure_quantum_solution(evolved_state) return { "strategy": EvolutionStrategy.QUANTUM_EVOLUTION, "quantum_solution": quantum_solution, "improvement": await self._estimate_quantum_improvement(quantum_solution), "evolution_step": EvolutionStep( step_id=f"quantum_{hashlib.md5(str(quantum_solution).encode()).hexdigest()[:8]}", strategy=EvolutionStrategy.QUANTUM_EVOLUTION, changes={"quantum_updates": quantum_solution}, improvement=0.25, # Estimation learning_rate=0.03 ) } async def _meta_learning_evolution(self, target_metrics: Dict[ImprovementMetric, float]) -> Dict[str, Any]: """Évolution par méta-apprentissage""" self.logger.info("🎓 Évolution par méta-apprentissage") # Apprentissage des patterns d'évolution evolution_patterns = await self._learn_evolution_patterns() # Génération de stratégies d'évolution optimisées optimized_strategies = await self._generate_optimized_evolution_strategies(evolution_patterns) # Application de la stratégie méta-optimisée meta_evolution_result = await self._apply_meta_evolution_strategy(optimized_strategies, target_metrics) return { "strategy": EvolutionStrategy.META_LEARNING, "meta_evolution": meta_evolution_result, "improvement": await self._estimate_meta_improvement(meta_evolution_result), "evolution_step": EvolutionStep( step_id=f"meta_{hashlib.md5(str(meta_evolution_result).encode()).hexdigest()[:8]}", strategy=EvolutionStrategy.META_LEARNING, changes={"meta_updates": meta_evolution_result}, improvement=0.3, # Estimation learning_rate=0.025 ) } async def _create_new_version(self, evolution_result: Dict[str, Any]) -> SystemVersion: """Crée une nouvelle version du système""" version_id = f"v{len(self.evolution_history) + 1}.0.0_{evolution_result['strategy'].value}" # Mise à jour des composants updated_components = await self._apply_evolution_changes( self.current_version.code_components, evolution_result ) # Calcul des nouvelles métriques new_metrics = await self._calculate_new_metrics(updated_components) return SystemVersion( version_id=version_id, code_components=updated_components, performance_metrics=new_metrics, improvement_score=evolution_result["improvement"], evolutionary_path=self.current_version.evolutionary_path + [evolution_result["strategy"].value] ) async def _validate_improvement(self, new_version: SystemVersion) -> bool: """Valide que la nouvelle version apporte une amélioration""" current_score = await self._calculate_overall_score(self.current_version.performance_metrics) new_score = await self._calculate_overall_score(new_version.performance_metrics) improvement_threshold = 0.02 # 2% d'amélioration minimum return new_score > current_score + improvement_threshold async def _analyze_current_performance(self) -> Dict[str, float]: """Analyse les performances actuelles du système""" # Simulation d'analyse de performance return { "response_time": random.uniform(0.8, 1.2), "accuracy": random.uniform(0.8, 0.95), "resource_usage": random.uniform(0.7, 1.3), "adaptability": random.uniform(0.6, 0.9) } async def _calculate_improvement_targets(self, current_performance: Dict[str, float]) -> Dict[ImprovementMetric, float]: """Calcule les cibles d'amélioration""" targets = {} for metric, baseline in self.performance_baseline.items(): current_value = current_performance.get(metric, baseline) improvement_needed = max(0, baseline - current_value) + self.improvement_targets.get( ImprovementMetric(metric), 0.1 ) targets[ImprovementMetric(metric)] = improvement_needed return targets async def _analyze_environment(self, environment_metrics: Dict[str, Any]) -> Dict[str, Any]: """Analyse l'environnement pour l'optimisation""" return { "environment_type": environment_metrics.get("type", "unknown"), "constraints": environment_metrics.get("constraints", {}), "opportunities": await self._identify_environment_opportunities(environment_metrics) } async def _evolutionary_adaptation(self, environment_analysis: Dict[str, Any]) -> SystemVersion: """Adaptation évolutive à l'environnement""" # Simulation d'adaptation adapted_components = await self._adapt_components_to_environment( self.current_version.code_components, environment_analysis ) return SystemVersion( version_id=f"{self.current_version.version_id}_adapted", code_components=adapted_components, performance_metrics=await self._calculate_environment_metrics(adapted_components, environment_analysis), improvement_score=0.1, evolutionary_path=self.current_version.evolutionary_path + ["environment_adaptation"] ) async def _environment_specific_tuning(self, version: SystemVersion, environment_analysis: Dict[str, Any]) -> SystemVersion: """Fine-tuning spécifique à l'environnement""" # Simulation de fine-tuning tuned_components = await self._fine_tune_components(version.code_components, environment_analysis) return SystemVersion( version_id=f"{version.version_id}_tuned", code_components=tuned_components, performance_metrics=await self._calculate_tuned_metrics(tuned_components, environment_analysis), improvement_score=version.improvement_score + 0.05, evolutionary_path=version.evolutionary_path + ["environment_tuning"] ) async def _extract_domain_knowledge(self, domain: str) -> Dict[str, Any]: """Extrait les connaissances d'un domaine spécifique""" return { "domain_patterns": await self._learn_domain_patterns(domain), "optimal_strategies": await self._extract_optimal_strategies(domain), "domain_constraints": await self._identify_domain_constraints(domain) } async def _adapt_knowledge_to_domain(self, source_knowledge: Dict[str, Any], target_domain: str) -> Dict[str, Any]: """Adapte les connaissances au domaine cible""" return { "transferred_patterns": await self._transfer_patterns(source_knowledge, target_domain), "adapted_strategies": await self._adapt_strategies(source_knowledge, target_domain), "domain_specific_optimizations": await self._create_domain_optimizations(target_domain) } async def _integrate_transferred_knowledge(self, transferred_knowledge: Dict[str, Any]) -> SystemVersion: """Intègre les connaissances transférées""" enhanced_components = await self._enhance_with_transferred_knowledge( self.current_version.code_components, transferred_knowledge ) return SystemVersion( version_id=f"{self.current_version.version_id}_transferred", code_components=enhanced_components, performance_metrics=await self._calculate_transferred_metrics(enhanced_components), improvement_score=0.15, evolutionary_path=self.current_version.evolutionary_path + ["knowledge_transfer"] ) async def _extract_learning_patterns(self, learning_tasks: List[Dict[str, Any]]) -> Dict[str, Any]: """Extrait les patterns d'apprentissage""" patterns = {} for task in learning_tasks: task_patterns = await self._analyze_learning_task(task) patterns[task["id"]] = task_patterns return { "common_patterns": await self._find_common_patterns(patterns), "optimization_strategies": await self._extract_optimization_strategies(patterns), "learning