In recent years, there has been a growing research interest in integrating machine learning
techniques into meta-heuristics for solving combinatorial optimization problems. This …

More than a decade after the first extensive overview on parameter control, we revisit the
field and present a survey of the state-of-the-art. We briefly summarize the development of …

Meta-optimization techniques for tuning algorithm parameters usually try to find optimal
parameter settings for a given computational budget allocated to the lower-level algorithm. If …

Memetic search is well known as one of the state-of-the-art metaheuristics for finding high-
quality solutions to NP-hard problems. Its performance is often attributable to appropriate …

Even though only my name is on the cover of this thesis, many people contributed to it. I
would like to use this first section to acknowledge these contributions. First I would like to …

Two fundamental challenges in local search based metaheuristics are how to determine
parameter configurations and design the underlying Local Search (LS) procedure. In this …

The present study applies Algorithm Selection (AS) to Adaptive Operator Selection (AOS) for
further improving the performance of the AOS methods. AOS aims at delivering high …

Evolutionary Algorithm is a well-known meta-heuristics para-digm capable of providing high-
quality solutions to computationally hard problems. As with the other meta-heuristics, its …

Configuring parameter settings for ant colony optimisation (ACO) based algorithms is a
challenging and time consuming task, because it usually requires evaluating a large number …

Current state-of-the-art medical deformable image registration (DIR) methods optimize a
weighted sum of key objectives of interest. Having a pre-determined weight combination that …