摘要
Evolutionary algorithms have been used to solve various optimization problems by iteratively refining solutions through the process inspired by natural evolution. To reduce the randomness inherent, multiple trials are often performed on a single problem, producing a wealth of data that may hold valuable optimization insights. However, traditional EAs usually discard this accumulated experience when tackling new optimization tasks. To address this issue, a novel evolutionary computation paradigm, i.e., learning-aided evolutionary optimization, was developed to exploit past optimization knowledge to enhance search efficiency. In this regard, we propose an offline knowledge learning (OKL) framework, consisting of two key phases: (1) extracting knowledge from past successful optimization runs and (2) applying the knowledge to guide the evolutionary search process. Specifically, we employ a neural network as the knowledge model to generate promising offspring during the optimization process. Additionally, an adaptive mechanism is introduced to dynamically control the rate at which the knowledge model is utilized. Experiments are conducted using genetic algorithm (GA) as the backbone optimizer, resulting in the OKL-GA approach. The results demonstrate that the OKL framework can effectively leverage previous optimization knowledge, enabling generation of fitter offspring and significantly improving performance.