Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the
goal of automating the design of heuristic methods to solve hard computational search …

Hybrid genetic algorithms have received significant interest in recent years and are being
increasingly used to solve real-world problems. A genetic algorithm is able to incorporate …

Over the last decade, memetic algorithms (MAs) have relied on the use of a variety of
different methods as the local improvement procedure. Some recent studies on the choice of …

Ill-posedness. Regularization. Stability. Uniqueness. To many engineers, the language of
inverse analysis projects a mysterious and frightening image, an image made even more …

Evolutionary algorithms (EAs) are robust stochastic optimisers that perform well over a wide
range of problems. Their robustness, however, may be affected by several adjustable …

Learning the optimal probabilities of applying an exploration operator from a set of
alternatives can be done by self-adaptation or by adaptive allocation rules. In this paper we …

Several techniques have been proposed to tackle the Adaptive Operator Selection (AOS)
issue in Evolutionary Algorithms. Some recent proposals are based on the Multi-armed …

Like engineering systems, biological systems must also operate effectively in the presence
of internal and external uncertainty—such as genetic mutations or temperature changes, for …

An important step toward self-tuning Evolutionary Algorithms is to design efficient Adaptive
Operator Selection procedures. Such a procedure is made of two main components: a credit …

Identifying contaminant sources in groundwater is important for developing effective
remediation strategies and identifying responsible parties in a contamination incident …