Like hyper-heuristics to select heuristics, hyper-heuristics to generate heuristics also use a set of known heuristics to start off.īut, unlike the other kind of hyper-heuristics, these ones first decompose the pre-existing heuristics into their basic components, proceeding to select components that can be used to create new heuristics to solve the problem instead of just selecting entire heuristics as they are and using them to solve computational problems.Īn iterative selection hyper-heuristic makes use of a chosen low-level heuristic to the current solution at each step of a search before it decides whether to accept or reject the newly created solution. This type of hyper-heuristics focuses on creating new heuristics by using components from existing known heuristics. If the solution is accepted, it is used to replace the incumbent solution, but if it is rejected, it is discarded. In this type of hyper-heuristics, we provide the hyper-heuristic framework with a set of well-known heuristics that can be used for solving the computational problem in question.Īt every stage, a component of the hyper-heuristic called the selection mechanism chooses a heuristic and applies it to an incumbent solution.Īnother component of the hyper-heuristic called the acceptance criterion decides whether to accept or reject the solution that was created from the heuristic that was picked by the selection mechanism. There are two types of hyper-heuristics: hyper-heuristics to select heuristics and hyper-heuristics to generate heuristics. The solution chosen or generated should be affordable and easy to implement, without requiring much expertise in heuristics or in the domain in which the problem lies.Ī hyper heuristic is essentially a high-level automated search methodology which explores a search space of low-level heuristics (neighbourhood or move operators, or metaheuristics) or heuristic components, for the purpose of solving computationally difficult problems. They seek to reduce the amount of domain knowledge in search methods. They can be considered to be s search methods that operate on lower-level heuristics or heuristic components.Įssentially, hyper-heuristics are high-level automated search methodologies that explore the search space of low-level heuristics or heuristic components to solve those difficult computational search problems. The state-of-the-art in hyper-heuristic research used today is made up of a set of methods that are broadly concerned with intelligently selecting or generating a suitable heuristic for a given situation. In the early 2000s, the term hyper-heuristic was defined to be a heuristic to choose heuristics, but the idea of designing high-level heuristic methodologies goes all the way back to the early 1960s. This stands in contrast to several approaches, which represent customised methods for a single problem domain or a narrow class of problem instances. Hyper-heuristics came on the scene as a way to increase the level of generality of search techniques for computational search problems. To achieve it, it often uses machine learning techniques. It does this to solve complex computational search problems that any of those simpler heuristics could not effectively solve on their own. A hyper-heuristic is a search heuristic that automates the selection, combination, generation, and adaptation of multiple simpler heuristics.
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