Scheduling is one of the core steps to efficiently exploit the capabilities of heterogeneous distributed computing systems and represents an NP-complete problem. Therefore, using meta-heuristic algorithms is a suitable approach in order to cope with its difficulty. In many meta-heuristic algorithms, generating individuals in the initial step has an important effect on the convergence behavior o...

Some games have abstract properties that can be used to design admissible heuristics on moves. These admissible heuristics are useful to speed up search. They work well with depth-bounded search algorithms such as Gradual Abstract Proof Search that select moves based on the distance to the goal. We analyze the benefits of these admissible heuristics on moves for rules generation and search. We ...

While static epidemic control, e.g. using vaccination, has been extensively studied for various network types, controlling epidemics dynamically remains an open issue. In this work, we first propose a general model formulation for the dynamic treatment allocation problem for the SusceptibleInfected-Susceptible diffusion model. Then, we investigate dynamic control strategies and further propose ...

Community detection based on modularity maximization is currently done with hierarchical as well as with partitioning heuristics, and, in a few papers, exact algorithms. Hierarchical heuristics aim at finding a set of nested partitions. They are in principle devised for finding a hierarchy of partitions implicit in the given network when it corresponds to some situation where hierarchy is obser...

Meta-heuristics are methods that sit on top of local search algorithms. They perform the function of avoiding or escaping a local optimum and/or premature convergence. The aim of this paper is to survey, compare and contrast meta-heuristics for local search. First, we present the technique of local search (or hill climbing as it is sometimes known). We then present a table displaying the attrib...

The performance of almost all parallel algorithms and systems can be improved by the use of heuristics that a ect the parallel execution. However, since optimal guidance usually depends on many di erent in uences, establishing such heuristics is often di cult. Due to the importance of heuristics for optimizing parallel execution, and the similarity of the problems that arise for establishing su...

This dissertation presents a study of the portfolio management problem applied to mutual fund investments. An approach based on Genetic Algorithms is followed and three different heuristics are proposed to forecast the rise or fall of a mutual fund. These heuristics are based in the concepts of daily variations, relevant maximums and minimums and simple moving averages. A prototype developed in...

To survive in a world where knowledge is limited, time is pressing, and deep thought is often an unattainable luxury, decision-makers must use bounded rationality. In this precis of Simple heuristics that make us smart, we explore fast and frugal heuristics—simple rules for making decisions with realistic mental resources. These heuristics enable smart choices to be made quickly and with a mini...

Online diagnosis methods require large computationally expensive diagnosis tasks to be decomposed into sets of smaller tasks so that time and space complexity constraints are not violated. This paper defines the distributed diagnosis problem in the Transcend qualitative diagnosis framework, and then develops heuristic algorithms for generating a set of local diagnosers that solve the global dia...