Hyper-heuristics could simply be defined as "heuristics to choose other heuristics" (Burke, Kendall, Newall, Hart, Ross, and Schulenburg, 2003). A heuristic is considered as a rule-of-thumb or "educated guess" that reduces the search required to find a solution. The difference between meta-heuristics and hyper-heuristics is that the former operate directly on the problem search space with the goal of finding optimal or near-optimal solutions. The latter, instead, operate on the heuristics search space (which consists of the heuristics used to solve the target problem). The goal then is finding or generating high-quality heuristics for a problem, for a certain class of instances of a problem, or even for a particular instance.
GP has been very successfully used as a hyperheuristic. For example, GP has evolved competitive SAT solvers (Bader-El-Den and Poli, 2007a, b; Fukunaga, 2002; Kibria and Li, 2006), state-of-the-art or better than state-of-the-art bin packing algorithms (Burke, Hyde, and Kendall, 2006; Burke, Hyde, Kendall, and Woodward, 2007; Poli, Woodward, and Burke, 2007), particle swarm optimisers (Poli, Di Chio, and Langdon, 2005; Poli, Langdon, and Holland, 2005), evolutionary algorithms (Oltean, 2005), and travelling salesman problem solvers (Keller and Poli, 2007a,b, c; Oltean and Dumitrescu, 2004).