Cortex/engineer/self_evolving_system.py

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