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 …

We address the important step of determining an effective subset of heuristics in selection
hyper-heuristics. Little attention has been devoted to this in the literature, and the decision is …

Parameter tuning is a challenging and time-consuming task, crucial to obtaining improved
metaheuristic performance. There is growing interest in cross-domain search methods …

HyFlex is a recently proposed software framework for implementing hyper-heuristics and
domain-independent heuristic optimisation algorithms [13]. Although it was originally …

This thesis centers on the topic of how to automatically combine multiple heuristics. For most
computationally challenging problems, there exist multiple heuristics, and it is generally the …

Selection hyper‐heuristics are generic search tools that dynamically choose, from a given
pool, the most promising operator (low‐level heuristic) to apply at each iteration of the …

The present study utilizes neural network to perform heuristic selection in selection hyper-
heuristics. Selection hyper-heuristics are problem-independent solvers, preferably benefited …

There is a growing interest towards self configuring/tuning automated general-purpose
reusable heuristic approaches for combinatorial optimisation, such as, hyper-heuristics …

Heuristics, metaheuristics and hyper-heuristics are search methodologies which have been
preferred by many researchers and practitioners for solving computationally hard …

Hyper-heuristics are a class of high-level search methodologies which operate over a
search space of heuristics rather than a search space of solutions. Hyper-heuristic research …