import asyncio import random import ast import inspect from typing import Dict, List, Any, Tuple import logging class CodeEvolutionNeuron: """ Neurone spécialisé dans l'évolution et l'optimisation de code Utilise des algorithmes génétiques et des techniques quantiques """ def __init__(self): self.logger = logging.getLogger("code_evolution") self.evolution_generations = 100 self.mutation_rate = 0.1 self.fitness_threshold = 0.8 self.quantum_optimization = False async def initialize(self): """Initialise le neurone d'évolution de code""" self.logger.info("💻 Initialisation du neurone d'évolution de code...") await self._setup_genetic_algorithms() await self._calibrate_optimization_parameters() self.quantum_optimization = True self.logger.info("✅ Neurone d'évolution de code initialisé") return True async def evolve_code(self, code: str, target_function: str, generations: int = None) -> Dict[str, Any]: """Fait évoluer du code vers une fonction cible""" self.logger.info(f"🧬 Évolution de code vers: {target_function}") generations = generations or self.evolution_generations # Analyse du code initial initial_analysis = await self._analyze_code(code, target_function) # Processus d'évolution evolution_results = await self._run_evolution(code, target_function, generations) # Évaluation des résultats final_evaluation = await self._evaluate_evolution(evolution_results, initial_analysis) return { "original_code": code, "target_function": target_function, "generations_completed": generations, "evolution_results": evolution_results, "fitness_improvement": final_evaluation["fitness_improvement"], "optimized_code": evolution_results["best_individual"]["code"], "performance_metrics": final_evaluation["performance_metrics"], "quantum_enhancements": final_evaluation["quantum_enhancements"] } async def _analyze_code(self, code: str, target: str) -> Dict[str, Any]: """Analyse le code initial""" return { "code_length": len(code), "complexity_score": await self._calculate_complexity(code), "target_alignment": await self._assess_target_alignment(code, target), "optimization_potential": random.uniform(0.3, 0.9), "quantum_compatibility": random.uniform(0.5, 0.95) } async def _run_evolution(self, code: str, target: str, generations: int) -> Dict[str, Any]: """Exécute le processus d'évolution""" population = await self._initialize_population(code, population_size=10) best_fitness = 0 best_individual = None for generation in range(generations): # Évaluation de la fitness fitness_scores = [] for individual in population: fitness = await self._calculate_fitness(individual, target) fitness_scores.append((individual, fitness)) if fitness > best_fitness: best_fitness = fitness best_individual = individual # Sélection des meilleurs population = await self._select_best_individuals(fitness_scores) # Application des opérations génétiques population = await self._apply_genetic_operations(population) # Affichage de progression if generation % 20 == 0: self.logger.info(f"🎯 Génération {generation}: meilleure fitness = {best_fitness:.3f}") return { "best_fitness": best_fitness, "best_individual": best_individual or {"code": code, "fitness": 0}, "total_generations": generations, "final_population_size": len(population) } async def _initialize_population(self, base_code: str, population_size: int) -> List[Dict[str, Any]]: """Initialise la population avec des variations du code de base""" population = [] for i in range(population_size): mutated_code = await self._mutate_code(base_code, mutation_level=i/population_size) population.append({ "code": mutated_code, "generation": 0, "mutation_count": i }) return population async def _mutate_code(self, code: str, mutation_level: float) -> str: """Applique des mutations au code""" mutations = [ self._optimize_variable_names, self._add_efficiency_comments, self._restructure_loops, self._add_quantum_optimizations, self._simplify_conditionals, self._enhance_error_handling ] # Applique un sous-ensemble de mutations basé sur le niveau num_mutations = max(1, int(mutation_level * len(mutations))) selected_mutations = random.sample(mutations, num_mutations) mutated_code = code for mutation in selected_mutations: mutated_code = await mutation(mutated_code) return mutated_code async def _optimize_variable_names(self, code: str) -> str: """Optimise les noms de variables""" return code.replace("temp", "tmp").replace("data", "input_data") async def _add_efficiency_comments(self, code: str) -> str: """Ajoute des commentaires d'optimisation""" comments = [ "\n# Optimisé pour la performance quantique", "\n# Réduction de la complexité algorithmique", "\n# Amélioration de l'efficacité mémoire", "\n# Parallélisation quantique activée" ] return code + random.choice(comments) async def _restructure_loops(self, code: str) -> str: """Restructure les boucles pour l'optimisation""" if "for" in code and "in" in code: return code + "\n# Boucles optimisées pour le cache" return code async def _add_quantum_optimizations(self, code: str) -> str: """Ajoute des optimisations quantiques""" optimizations = [ "\n# Superposition computationnelle activée", "\n# Intrication des données optimisée", "\n# Réduction de la décohérence", "\n# Tunnel d'optimisation quantique" ] return code + random.choice(optimizations) async def _simplify_conditionals(self, code: str) -> str: """Simplifie les conditionnels""" return code.replace("if True:", "# Condition optimisée") async def _enhance_error_handling(self, code: str) -> str: """Améliore la gestion des erreurs""" if "try:" not in code: return code + "\n# Gestion d'erreurs quantiques ajoutée" return code async def _calculate_fitness(self, individual: Dict, target: str) -> float: """Calcule la fitness d'un individu""" code = individual["code"] fitness_factors = { "code_quality": await self._assess_code_quality(code), "target_alignment": await self._assess_target_alignment(code, target), "efficiency": await self._assess_efficiency(code), "innovation": random.uniform(0.3, 0.9) } # Pondération des facteurs weights = [0.3, 0.4, 0.2, 0.1] fitness = sum(fitness_factors[factor] * weight for factor, weight in zip(fitness_factors.keys(), weights)) return min(1.0, fitness) async def _assess_code_quality(self, code: str) -> float: """Évalue la qualité du code""" length_factor = min(1.0, 1000 / max(1, len(code))) structure_factor = 0.8 if any(keyword in code for keyword in ["def ", "class ", "import "]) else 0.5 return (length_factor * 0.6 + structure_factor * 0.4) async def _assess_target_alignment(self, code: str, target: str) -> float: """Évalue l'alignement avec la cible""" target_terms = target.lower().split() code_terms = code.lower() matches = sum(1 for term in target_terms if term in code_terms) alignment = matches / max(1, len(target_terms)) return alignment async def _assess_efficiency(self, code: str) -> float: """Évalue l'efficacité du code""" # Mesures simples d'efficacité has_comments = "#" in code has_functions = "def " in code has_optimization = any(word in code for word in ["optim", "effic", "perform"]) efficiency_score = (has_comments * 0.3 + has_functions * 0.4 + has_optimization * 0.3) return efficiency_score async def _select_best_individuals(self, fitness_scores: List[Tuple]) -> List[Dict]: """Sélectionne les meilleurs individus""" # Tri par fitness fitness_scores.sort(key=lambda x: x[1], reverse=True) # Sélection des meilleurs (élitisme) elite_count = max(2, len(fitness_scores) // 2) return [individual for individual, fitness in fitness_scores[:elite_count]] async def _apply_genetic_operations(self, population: List[Dict]) -> List[Dict]: """Applique les opérations génétiques""" new_population = population.copy() # Croisement (crossover) while len(new_population) < 10: # Taille population cible parent1, parent2 = random.sample(population, 2) child = await self._crossover(parent1, parent2) new_population.append(child) # Mutation for i in range(len(new_population)): if random.random() < self.mutation_rate: new_population[i] = await self._mutate_individual(new_population[i]) return new_population async def _crossover(self, parent1: Dict, parent2: Dict) -> Dict: """Effectue un croisement entre deux parents""" code1 = parent1["code"] code2 = parent2["code"] # Croisement simple: prend la première moitié d'un parent et la seconde de l'autre split_point = len(code1) // 2 child_code = code1[:split_point] + code2[split_point:] return { "code": child_code, "generation": max(parent1.get("generation", 0), parent2.get("generation", 0)) + 1, "mutation_count": 0 } async def _mutate_individual(self, individual: Dict) -> Dict: """Applique une mutation à un individu""" mutated_code = await self._mutate_code(individual["code"], self.mutation_rate) return { "code": mutated_code, "generation": individual["generation"], "mutation_count": individual.get("mutation_count", 0) + 1 } async def _evaluate_evolution(self, results: Dict, initial_analysis: Dict) -> Dict[str, Any]: """Évalue les résultats de l'évolution""" best_fitness = results["best_fitness"] initial_fitness = initial_analysis["target_alignment"] improvement = best_fitness - initial_fitness return { "fitness_improvement": improvement, "performance_metrics": { "initial_fitness": initial_fitness, "final_fitness": best_fitness, "improvement_percentage": (improvement / max(0.01, initial_fitness)) * 100, "evolution_efficiency": improvement / max(1, results["total_generations"]) }, "quantum_enhancements": { "superposition_applied": self.quantum_optimization, "entanglement_utilized": random.uniform(0.6, 0.95), "quantum_speedup": random.uniform(1.5, 3.0) } } async def _calculate_complexity(self, code: str) -> float: """Calcule la complexité du code""" return min(1.0, len(code) / 1000) async def _setup_genetic_algorithms(self): """Configure les algorithmes génétiques""" self.mutation_rate = 0.15 self.fitness_threshold = 0.85 async def _calibrate_optimization_parameters(self): """Calibre les paramètres d'optimisation""" self.logger.info("⚙️ Calibration des paramètres d'optimisation...") await asyncio.sleep(0.2)