Hyper-heuristics aim at interchanging different solvers while solving a problem. The idea is
to determine the best approach for solving a problem at its current state. This way, every time …

Literature is prolific with metaheuristics for solving continuous optimisation problems. But, in
practice, it is difficult to choose one appropriately for several reasons. First and …

Literature is prolific with metaheuristics for solving continuous optimisation problems. But, in
practice, it is difficult to choose one appropriately. Moreover, it is necessary to determine a …

Recent years have witnessed a growing interest in automatic learning mechanisms and
applications. The concept of hyper-heuristics, algorithms that either select among existing …

Metaheuristics (MHs) are proven powerful algorithms for solving non-linear optimisation
problems over discrete, continuous, or mixed domains. Applications have ranged from basic …

When dealing with optimisation problems, Metaheuristics (MHs) quickly come to our minds.
A quick literature review reveals a vast universe of MHs. Although the metaphors behind …

Hyper-heuristics are potent techniques that represent the synergy of low-level heuristics
when solving optimization problems. This synergy usually leads to better solutions. Similarly …

It is common to find that training of selection hyper-heuristics is done perturbatively. The
process usually starts with a random selection module and iterates over a set of instances …

Many of the works conducted on algorithm selection strategies—methods that choose a
suitable solving method for a particular problem—start from scratch since only a few …

A plethora of combinatorial optimization problems can be linked to real-life decision
scenarios. Even nowadays, more diverse and complex problems are popping up. One of …