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-heuristics comprise a set of approaches that aim to automate the development of
computational search methodologies. This chapter overviews previous categorisations of …

Metaheuristics, being tailored to each particular domain by experts, have been successfully
applied to many computationally hard optimization problems. However, once implemented …

Hyper-heuristics are a domain-independent problem solving approach where the main task
is to select effective chains of problem-specific low-level heuristics on the fly for an unseen …

In this paper, we propose a new hyper-heuristic approach that uses reinforcement learning
to automatically learn the selection of low-level heuristics across a wide range of problem …

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

The present study introduces algorithm selection on selection hyper-heuristics. Hyper-
heuristics are known as problem-independent methods utilized to solve different instances …

For multi-objective optimization problems, obtaining a uniformly distributed approximation
set is among the most important issues. During the past decades, various diversity …

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

Hyper-heuristics are widely used for solving numerous complex computational search
problems because of their intrinsic capability to generalize across problem domains. The fair …