Associative Constructive Evolution: Enhancing Metaheuristics through Hebbian-Learned Generative Guidance
This work addresses the inefficiency of metaheuristics in accumulating knowledge for researchers and practitioners in optimization, representing an incremental advancement by integrating learned guidance into existing algorithms.
The paper tackled the problem of metaheuristics lacking mechanisms to accumulate and reuse procedural knowledge from successful search trajectories, proposing the Associative Constructive Evolution (ACE) framework, which enhanced Particle Swarm Optimization and Evolutionary Algorithms with learned generative guidance, resulting in a 27.5% increase in success rate and 49.6% reduction in convergence time for maze navigation, and a 10.1% fitness improvement with 126 discovered macro-operations for molecular design.
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.