DEAL: Dual-Pool Evolutionary Algorithm with LLM-Driven Adaptive Operator Selection

Abstract

In evolutionary algorithms (EAs), static operator configurations often depend on human expertise and may lead to search stagnation. Existing adaptive methods either overlook real-time optimization states or require substantial computational overhead. Recent LLM-based methods support adaptive decision-making; however, applying an LLM to a single population requires the model to resolve conflicting exploration and exploitation objectives, which may reduce the reliability of operator selection. To address this limitation, this paper proposes DEAL, a Dual-Pool Evolutionary Algorithm with LLM-Driven Adaptive Operator Selection. DEAL separates the population into an exploitation pool and an exploration pool, enabling the LLM to pursue different search objectives: refining promising solutions in the exploitation pool and exploring alternative candidate structures in the exploration pool. DEAL further incorporates an offline experience collection module and a structured prompt template that jointly provide operator information, population status, and relevant historical evidence. Benchmark experiments on EUC-2D TSP instances show that DEAL reduces the average optimality gap on most tested categories compared with conventional baselines, suggesting the effectiveness of dual-pool LLM-driven operator scheduling.