AI Agent系列（8）：自进化架构的动力学实现

AI Agent系列（8）：自进化架构的动力学实现

二、自进化架构的实现机制

1. 神经达尔文选择算法

import numpy as np

class NeuralDarwinism:
    def __init__(self, population_size=50):
        self.population = self._initialize_population(population_size)
  
    def _initialize_population(self, size):
        # 生成随机拓扑架构编码（维度：层数×连接密度）
        return [np.random.rand(8,4) * 2 - 1 for _ in range(size)]

    def synaptic_selection(self, fitness_scores):
        """基于适应度的突触权重自然选择"""
        probabilities = np.exp(fitness_scores) / np.sum(np.exp(fitness_scores))
        selected_idx = np.random.choice(len(self.population), p=probabilities)
        return self.population[selected_idx]

    def cognitive_mutation(self, genome, mutation_rate=0.15):
        """受量子隧穿启发的认知变异算子"""
        mask = np.random.binomial(1, mutation_rate, size=genome.shape)
        return genome + mask * (np.random.randn(*genome.shape) * 0.2)

class HyperdimensionalRecombiner:
    def __init__(self, embedding_dim=512):
        self.rotation_matrix = np.random.randn(embedding_dim, embedding_dim)
      
    def conceptual_crossover(self, parentA, parentB):
        """在超球面上进行维度纠缠重组"""
        hybrid = (parentA + 1j*parentB) @ self.rotation_matrix
        return np.real(hybrid), np.imag(hybrid)

2. 拓扑基因编码策略

认知基因组代数表达式
令认知函数