Associative Constructive Evolution: Enhancing Metaheuristics through Hebbian-Learned Generative Guidance

2026-03-31

AI Summary

Traditional metaheuristic algorithms struggle to accumulate and reuse successful experiences from their search processes. This work proposes the Associative Constructive Evolution (ACE) framework, which leverages a Generative Construction Automaton (GCA) to extract co-occurring successful operation patterns from the search trajectories of metaheuristics such as evolutionary algorithms (EA) and particle swarm optimization (PSO). By integrating Hebbian learning, guided sampling, and symbolic abstraction, ACE autonomously generates reusable macro-operations, enabling knowledge-driven intelligent search. Experimental results demonstrate that ACE-PSO achieves a 27.5% higher success rate and 49.6% faster convergence in maze navigation tasks, while ACE-EA improves fitness by 10.1% in molecular design and automatically discovers 126 chemically interpretable macro-operations.

Technology Category

Search and Optimization: Metareasoning and MetaheuristicsMachine Learning: Evolutionary LearningConstraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphsSearch and Retrieval-Augmented AI: Agentic searchSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• metaheuristics
• procedural knowledge
• search trajectories
• knowledge reuse
• optimization

Innovation

• Associative Constructive Evolution
• Hebbian learning
• Generative Construction Automaton
• metaheuristics
• symbolic abstraction