#!/usr/bin/env python3 """ Neurone d'Évolution de Code Optimisation et évolution algorithmique avec approche quantique """ 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)