Choosing the Fittest Subset of Low Level Heuristics in a Hyperheuristic Framework

An empirical study of hyperheuristics for managing very large sets of low level heuristics

“…The number of low level heuristics available to the hyperheuristics is large compared to the number of those low level heuristics we will apply in constructing a solution, and new approaches are needed to learn the effectiveness of low level heuristics in this case. (Chakhlevitch and Cowling, 2005) find that when selecting a small random subset of low level heuristics results can be erratic across different problem instances indicating that a reduced subset is not as good as a large set.…”

“…The hyperheuristic method does not need to be problem specific, and hence a single hyperheuristic method has the advantage that it can work generally across many problem models and instances, given the right set of low level heuristics and solution quality metrics. There is good evidence to date that hyperheuristics are effective across a range of problems, and this effectiveness arguably arises since having a large collection of low level heuristics means that the solution landscape for one or more of these low level heuristics is likely to provide a good search direction (Chakhlevitch and Cowling, 2005) (Colledge, 2009) ).…”

“…In some cases low level heuristics are parameterised, or composed by "multiplying" together components (Remde et al, 2007) (Chakhlevitch and Cowling, 2005), which can give rise to hundreds or even thousands of heuristics (Cowling and Chakhlevitch, 2003). In such a case, deciding in reasonable time which heuristics to use may be difficult.…”

“…In such a case, deciding in reasonable time which heuristics to use may be difficult. However, there is evidence that having such a rich selection of low level heuristics may yield better results for complex problems in the long run, although it is difficult to know in advance which low level heuristics will prove effective (Chakhlevitch and Cowling, 2005). (Remde et al, 2007) studied the low level heuristics used by a greedy hyperheuristic HyperGreedy (which picks the best performing low level heuristic at each iteration) and found that a quarter of the low level heuristics were never used and half of the low level heuristics were effective less than one percent of the time.…”

Effective learning hyper-heuristics for the course timetabling problem

“…As discussed in (Misir et al, 2013), generality can be across various problem domains (Ochoa et al, 2012a,b) or across various heuristic sets (Chakhlevitch and Cowling, 2005;Misir et al, 2010). Generality has also been related to the ability to solve several variants of the same problem, which is achieved by designing problem models with increased generality.…”