Hyper-heuristics have emerged as a way to raise the level of generality of search techniques
for computational search problems. This is in contrast to many approaches, which represent …

Hyper-heuristic approaches aim to automate heuristic design in order to solve multiple
problems instead of designing tailor-made methodologies for individual problems. Hyper …

Harmony search algorithm (HSA) is a relatively new nature-inspired algorithm. It evolves
solutions in the problem search space by mimicking the musical improvisation process in …

Hyper-heuristics aim to automate the heuristic selection process in order to operate well
across different problem instances, or even across different problem domains. A traditional …

An important challenge within hyper-heuristic research is to design search methodologies
that work well, not only across different instances of the same problem, but also across …

Nurse rostering is a well-known highly constrained scheduling problem requiring
assignment of shifts to nurses satisfying a variety of constraints. Exact algorithms may fail to …

Metaheuristics provide high-level instructions for designing heuristic optimisation algorithms
and have been successfully applied to a range of computationally hard real-world problems …

This study provides a new hyper-heuristic design using a learning-based heuristic selection
mechanism together with an adaptive move acceptance criterion. The selection process was …

Despite the success of heuristic search methods in solving real-world computational search
problems, it is often still difficult to easily apply them to new problems, or even new instances …

We propose two adaptive variants of a multiple neighborhood iterated local search
algorithm. These variants employ online learning techniques, also called adaptive operation …