Abstract
Limited attention has been paid to assessing the generality performance of hyper-heuristics. The performance of hyper-heuristics has been predominately assessed in terms of optimality which is not ideal as the aim of hyper-heuristics is not to be competitive with state of the art approaches but rather to raise the level of generality, i.e. the ability of a technique to produce good results for different problem instances or problems rather than the best results for some instances and poor results for others. Furthermore from existing literature in this area it is evident that different hyper-heuristics aim to achieve different levels of generality and need to be assessed as such. To cater for this the paper firstly presents a new taxonomy of four different levels of generality that can be attained by a hyper-heuristic based on a survey of the literature. The paper then proposes a performance measure to assess the performance of different types of hyper-heuristics at the four levels of generality in terms of generality rather than optimality. Three case studies from the literature are used to demonstrate the application of the generality performance measure. The paper concludes by examining how the generality measure can be combined with measures of other performance criteria, such as optimality, to assess hyper-heuristic performance on more than one criterion.
Highlights
Hyper-heuristics is an emerging technique that has proven to be effective at solving various problems including educational timetabling, vehicle routing, personnel scheduling and packing problems, amongst others [1]
The early work includes that presented in [7], where an adaptive dynamic heuristic set (ADHS) strategy was used for heuristic selection, and an adaptive iteration limited list based threshold accepting (AILLA) approach was used for move acceptance
We propose the standard deviation of differences (SDD) as a measure to assess how well a hyperheuristic performs over a set of problem instances
Summary
Hyper-heuristics is an emerging technique that has proven to be effective at solving various problems including educational timetabling, vehicle routing, personnel scheduling and packing problems, amongst others [1]. In some studies the performance of the hyper-heuristic is compared to that of state-of-the-art approaches [4], [5] Such assessment is not appropriate against the main aim of a hyperheuristic, to produce good results over a problem set rather than best results for certain problem instances without considering its general performance across others. These can range from performing well on a set of problem instances for a single problem [6] to achieving generality across multiple problem domains [7]. A performance measure to assess the performance of hyper-heuristics in terms of generality
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