Metaheuristic algorithms such as Particle Swarm Optimization (PSO) and Evolutionary Algorithms (EA) excel at exploring solution spaces but lack mechanisms to accumulate and reuse procedural knowledge from successful search trajectories. This paper proposes Associative Constructive Evolution (ACE), a framework that enhances metaheuristics through learned generative guidance. ACE introduces a Generative Construction Automaton (GCA) -- a probabilistic model over operation sequences -- coupled with the base metaheuristic in a synergistic loop: the metaheuristic explores and provides trajectory samples, while the GCA consolidates successful patterns and guides future exploration. Three mechanisms realize this cooperation: Hebbian weight consolidation that strengthens associations between co-successful operations, guided sampling that biases search toward learned high-quality regions, and symbolic abstraction that extracts frequent patterns into reusable macro-operations. Experiments integrating ACE with EA and PSO on molecular design and maze navigation demonstrate consistent improvements. ACE-PSO achieves a 27.5% increase in success rate while reducing convergence time by 49.6%. In molecular design, ACE-EA improves fitness by 10.1% with 126 chemically interpretable macro-operations automatically discovered.

翻译：元启发式算法（如粒子群优化算法PSO和进化算法EA）在探索解空间方面表现出色，但缺乏积累和复用成功搜索轨迹中程序性知识的机制。本文提出联想式构造进化（ACE）框架，通过基于学习的生成引导增强元启发式算法。ACE引入生成式构造自动机（GCA）——一种操作序列上的概率模型——并与基础元启发式算法形成协同循环：元启发式算法进行探索并提供轨迹样本，而GCA整合成功模式并引导后续探索。三种机制实现这种协作：赫布权重巩固法（强化共成功操作间的关联），引导采样法（将搜索偏向于已学习的高质量区域），以及符号抽象法（将频繁模式提取为可复用的宏观操作）。在分子设计与迷宫导航任务上，将ACE与EA及PSO集成的实验表明具有一致性改进。ACE-PSO的成功率提升27.5%，同时收敛时间减少49.6%。在分子设计中，ACE-EA自动发现126个化学可解释的宏观操作，适应度提升10.1%。