The paper provides a comprehensive survey of hyper-heuristics, a set of approaches aimed at automating the design of heuristic methods to solve complex computational search problems. Hyper-heuristics operate on a search space of heuristics rather than directly on the solution space of the problem. The term was first used in 2000 to describe heuristics that choose other heuristics for combinatorial optimization. The paper discusses the historical roots of hyper-heuristics, which can be traced back to the 1960s, and outlines two main categories: heuristic selection and heuristic generation. It reviews various approaches, including those based on constructive and perturbative low-level heuristics, and highlights the use of machine learning techniques for online and offline learning. The paper also explores the relationship between the heuristic search space and the solution space, and suggests future research directions, such as hybrid methodologies and the exploration of additional application domains.The paper provides a comprehensive survey of hyper-heuristics, a set of approaches aimed at automating the design of heuristic methods to solve complex computational search problems. Hyper-heuristics operate on a search space of heuristics rather than directly on the solution space of the problem. The term was first used in 2000 to describe heuristics that choose other heuristics for combinatorial optimization. The paper discusses the historical roots of hyper-heuristics, which can be traced back to the 1960s, and outlines two main categories: heuristic selection and heuristic generation. It reviews various approaches, including those based on constructive and perturbative low-level heuristics, and highlights the use of machine learning techniques for online and offline learning. The paper also explores the relationship between the heuristic search space and the solution space, and suggests future research directions, such as hybrid methodologies and the exploration of additional application domains.