# Automated Generation of Hybrid Metaheuristics Using Learning-to-Rank
## 2 Related Work
### Hyper-Heuristics and Automated Algorithm Generation Based on Machine Learning  
Hyper-heuristics and automated algorithm generation and composition are becoming important directions in the intelligent development of algorithm design. The core idea lies in employing higher-level heuristic search and learning mechanisms to dynamically select and combine multiple underlying heuristic algorithms, thereby aiming to solve complex problems [9]. In recent years, applying machine learning to the design of efficient and robust metaheuristic algorithms has become a research hotspot, with many machine learning-enhanced metaheuristic algorithms demonstrating superior performance. [PERSON] [10] investigated the integration of machine learning and metaheuristic algorithms and established a unified classification system based on search components, including optimization objectives and high- and low-level components of metaheuristic algorithms, with the aim of encouraging researchers in the optimization field to explore synergistic mechanisms between machine learning and metaheuristics. To address the challenge of ranking massive volumes of documents in information retrieval systems, a Learning-to-Rank method combining an improved Genetic Algorithm with the Nelder-Mead approach was proposed [18]. By optimizing the ranking weight coefficients, this method significantly improved ranking metrics such as NDCG. [PERSON] et al. [11] enhanced the behavior selection mechanism of the WOA using SARSA, thereby improving the algorithm's solution performance. [PERSON] et al. [12] developed a preference-driven interactive learning framework aimed at enhancing both the convergence rate and diversity of traditional multi-objective methods. By periodically collecting user feedback and dynamically optimizing the search direction using an LTR model, this approach effectively guides the heuristic algorithm toward rapid solution discovery. [PERSON] et al. [19] proposed a Bayesian model-guided hyper-heuristic framework, which introduces a heuristic selection strategy assisted by probabilistic graphical models for single-objective continuous optimization problems, thereby achieving automation in algorithm selection and scheduling. [PERSON] et al. [1] developed a general search framework that employs DQN and proximal policy optimization methods to realize automated design of metaheuristic algorithms. Similarly, [PERSON] et al. [20] utilized the general search framework as a foundation for analyzing algorithm components in the automated design of hyper-heuristics. Within this framework, novel metaheuristic algorithms were automatically designed for CVRPTW problems. Hyper-heuristic algorithms and automated algorithm generation frameworks are gradually moving away from heavy reliance on expert knowledge, leveraging machine learning techniques like reinforcement learning and Learning to Rank to drive the optimization process toward greater automation and intelligence. This development trend lays a solid theoretical and technological foundation for building efficient and adaptive optimization algorithm systems. In addition, reinforcement learning, with its advantage of dynamic interaction, has demonstrated great potential in exploring complex state spaces. However, its strong dependence on reward mechanisms and the high cost of training limit its applicability in certain ranking tasks. In contrast, Learning to Rank, with its ranking-oriented nature, directly models the relationship between features and ranking objectives through supervised or weakly supervised learning. This not only enables algorithm generation and composition strategies but also offers strong interpretability and lightweight model characteristics. This indicates that Learning to Rank holds significant potential in algorithm combinatorial optimization, warranting further exploration of its effectiveness in enhancing algorithm adaptability and performance.