A selection hyper-heuristic is used to minimise the objective functions
of a well-known set of benchmark problems. The resulting sequences of
low level heuristic selections and objective function values are used to generate a database of heuristic selections. The sequences in the database are
broken down into subsequences and the ...
A selection hyper-heuristic is used to minimise the objective functions
of a well-known set of benchmark problems. The resulting sequences of
low level heuristic selections and objective function values are used to generate a database of heuristic selections. The sequences in the database are
broken down into subsequences and the mathematical concept of a logarithmic return is used to discriminate between “effective” subsequences,
which tend to decrease the objective value, and “disruptive” subsequences,
which tend to increase the objective value. These subsequences are then
employed in a sequenced based hyper-heuristic and evaluated on an unseen set of benchmark problems. Empirical results demonstrate that the
“effective” subsequences perform significantly better than the “disruptive” subsequences across a number of problem domains with 99% confidence. The identification of subsequences of heuristic selections that can
be shown to be effective across a number of problems or problem domains
could have important implications for the design of future sequence based
hyper-heuristics.