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12.8 GP to Create Searchers and Solvers - Hyper-heuristics

Hyper-heuristics could simply be defined as "heuristics to choose other heuristics" (Burke, Kendall, Newall, Hart, Ross, and Schulenburg2003). 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 Poli2007a, bFukunaga2002Kibria and Li2006), state-of-the-art or better than state-of-the-art bin packing algorithms (Burke, Hyde, and Kendall2006Burke, Hyde, Kendall, and Woodward2007Poli, Woodward, and Burke2007), particle swarm optimisers (Poli, Di Chio, and Langdon2005Poli, Langdon, and Holland2005), evolutionary algorithms (Oltean2005), and travelling salesman problem solvers (Keller and Poli2007a,b, cOltean and Dumitrescu2004).


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