An experimental analysis of a GP hyperheuristic approach for evolving low-cost heuristics for profile reductions

Researchers used graph-theory approaches to design the state-of-theart low-cost heuristics for profile reduction. This paper evolves and selects four low-cost heuristics for profile reduction using a genetic programming hyperheuristic approach. This paper evaluates the resulting heuristics for profile reduction from the genetic programming hyperheuristic approach in two application areas against the low-cost heuristics for solving the problem. The results obtained on a set of standard benchmark matrices taken from the SuiteSparse sparse matrix collection indicate that the resulting heuristics from the genetic programming hyperheuristic approach does not compare favorably with a high-quality heuristics for profile reduction.