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 …

Algorithms for solving hard optimization problems typically have several parameters that
need to be set appropriately such that some aspect of performance is optimized. In this …

Modern optimization algorithms typically require the setting of a large number of parameters
to optimize their performance. The immediate goal of automatic algorithm configuration is to …

The identification of performance-optimizing parameter settings is an important part of the
development and application of algorithms. We describe an automatic framework for this …

In this paper we present an extension of ant colony optimization (ACO) to continuous
domains. We show how ACO, which was initially developed to be a metaheuristic for …

Hyper-heuristics represent a novel search methodology that is motivated by the goal of
automating the process of selecting or combining simpler heuristics in order to solve hard …

The determination of appropriate values for free algorithm parameters is a challenging and
tedious task in the design of effective algorithms for hard problems. Such parameters include …

Ant colony optimization (ACO) is an optimization technique that was inspired by the foraging
behaviour of real ant colonies. Originally, the method was introduced for the application to …

Finding appropriate values for the parameters of an algorithm is a challenging, important,
and time consuming task. While typically parameters are tuned by hand, recent studies have …

State-of-the-art solvers for mixed integer programming (MIP) problems are highly
parameterized, and finding parameter settings that achieve high performance for specific …