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1. Practitioners now need no longer implement heuristics, but rather simply specify the problem domain.
2. It eliminates the domain-barrier, putting hyper-heuristics on the same 'informed' status about the problem as problem-specific metaheuristics.
3. Given the whitebox nature of the problem description, the infamous 'No Free Lunch' theorem (which essentially states that, for black box problems, Simulmated Annealing with an infinite annealing schedule is, on average, as good as any other approach) no longer applies.

1. Practitioners now need no longer implement heuristics, but rather simply specify the problem domain.
2. It eliminates the domain-barrier, putting hyper-heuristics on the same 'informed' status about the problem as problem-specific metaheuristics.
3. With a whitebox problem description, the infamous 'No Free Lunch' theorem (which essentially states that, considered over the space of all black box problems, Simulated Annealing with an infinite annealing schedule is, on average, as good as any other approach) no longer applies.

The application area for hyper-heuristics is therefore the same as metaheuristics. Their applicability (relative to metaheuristics) is as a 'rapid prototyping tool': the original motivation was to allow non-expert practitioners to apply metaheuristics to their specific optimization problem (e.g. "travelling-salesman plus time-windows plus bin-packing") without requiring expertise in the highly-specific problem domain. The idea was that this could be done by:

The application area for hyper-heuristics is therefore the same as metaheuristics. Their applicability (relative to metaheuristics) is as a 'rapid prototyping tool': the original motivation was to allow non-expert practitioners to apply metaheuristics to their specific optimization problem (e.g. "Travelling-Salesman (TSP) plus time-windows plus bin-packing") without requiring expertise in the highly-specific problem domain. The idea was that this could be done by:

In contrast, the original motivator for selective hyper-heuristics that researchers could create a hyper-heuristic solver that was likely to work well in an unseen problem domain, using only simple randomized heuristics.

The way that this has traditionally been implemented was via the introduction of the 'hyper-heuristic domain barrier' (see figure, below), whereby generality across problem domains is claimed to be achievable by preventing the solver from having knowledge of the domain it is operating on. Instead, it would solve the problem by operating only on opaque integer indices corresponding to the available heuristics (e.g. in the manner of the 'Multi-armed Bandit Problem').

In practice, this 'domain blind' approach has not resulted in solutions of sufficient quality. In order to achieve results anywhere comparable to problem-specific metaheuristics, hyper-heuristic researchers have to implement complex problem-specific heuristics, thereby failing in the goal of rapid prototyping.

In contrast, the original motivator for selective hyper-heuristics was that researchers would be able to create a hyper-heuristic solver that was then likely to work well in an unseen problem domain, using only simple randomized heuristics.

The way that this has traditionally been implemented was via the introduction of the 'hyper-heuristic domain barrier' (see figure, below), whereby generality across problem domains is claimed to be achievable by preventing the solver from having knowledge of the domain on which it is operating. Instead, it would solve the problem by operating only on opaque integer indices into a list of available heuristics (e.g. in the manner of the 'Multi-armed Bandit Problem').

In practice, this 'domain blind' approach has not resulted in solutions of sufficient quality. In order to achieve results anywhere comparable to problem-specific metaheuristics, hyper-heuristic researchers have had to implement complex problem-specific heuristics, thereby failing in the goal of rapid prototyping.