The Frequency Assignment Problem (FAP) is one of the key issues in the design of GSM networks (Global System for Mobile communications), and will remain important in the foreseeable future. There are many versions of FAP, most of them benchmarking-like problems. We use a formulation of FAP, developed in published work, that focuses on aspects which are relevant for real-world GSM networks. In this paper, we have designed, adapted, and evaluated several types of metaheuristic for different time ranges. After a detailed statistical study, results indicate that these metaheuristics are very appropriate for this FAP. New interference results have been obtained, that significantly improve those published in previous research.

Non-cryptographic hash functions (NCHFs) have an immense number of applications, ranging from compilers and databases to videogames and computer networks. Some of the most important NCHF have been used by major corporations in commercial products. This practical success demonstrates the ability of hashing systems to provide extremely efficient searches over unsorted sets. However, very little research has been devoted to the experimental evaluation of these functions. Therefore, we evaluated the most widely used NCHF using four criteria as follows: collision resistance, distribution of outputs, avalanche effect, and speed. We identified their strengths and weaknesses and found significant flaws in some cases. We also discuss our conclusions regarding general hashing considerations such as selection of the compression map. Our results should assist practitioners and engineers in making more informed choices regarding which function to use for a particular problem. This does not apply to cryptographic hash functions that use a variety of systems other than Merkle-Damgård. This is because this construction scheme is no longer considered safe, because different cryptanalysis studies have exposed some weaknesses that are considered important for cryptographic applications. Alternative schema include HAsh Iterative FrAmework (HAIFA) [6], wide-pipe construction [7], and sponge construction [8,9].

The video game industry is an emerging market which continues to expand. From its early beginning, developers have focused mainly on sound and graphical applications, paying less attention to developing game bots or other kinds of nonplayer characters (NPCs). However, recent advances in artificial intelligence offer the possibility of developing game bots which are dynamically adjustable to several difficulty levels as well as variable game environments. Previous works reveal a lack of swarm intelligence approaches to develop these kinds of agents. Considering the potential of particle swarm optimization due to its emerging properties and self-adaptation to dynamic environments, further investigation into this field must be undertaken. This research focuses on developing a generic framework based on swarm intelligence, and in particular on ant colony optimization, such as it allows general implementation of real-time bots that work over dynamic game environments. The framework has been adapted to allow the implementation of intelligent agents for the classical game Ms. Pac-Man. These were trialed at the Ms. Pac-Man competitions held during the 2011 International Congress on Evolutionary Computation.

Nowadays, mobile communications are experiencing a strong growth, being more and more indispensable. One of the key issues in the design of mobile networks is the Frequency Assignment Problem (FAP). This problem is crucial at present and will remain important in the foreseeable future. Real world instances of FAP typically involve very large networks, which can only be handled by heuristic methods. In the present work, we are interested in optimizing frequency assignments for problems described in a mathematical formalism that incorporates actual interference information, measured directly on the field, as is done in current GSM networks. To achieve this goal, a range of metaheuristics have been designed, adapted, and rigourously compared on two actual GSM networks modeled according to the latter formalism. In order to generate quickly and reliably high quality solutions, all metaheuristics combine their global search capabilities with a local-search method specially tailored for this domain. The experiments and statistical tests show that in general, all metaheuristics are able to improve upon results published in previous studies, but two of the metaheuristics emerge as the best performers: a populationFrancisco Luna, Antonio J. Nebro, and Enrique Alba Universidad de Málaga, Spain E-mail: {flv, antonio, eat}@lcc.uma.es César Estébanez, Ricardo Aler, and José M. Valls Universidad Carlos III de Madrid, Spain E-mail: {cesteban, aler, jvalls}@inf.uc3m.es Coromoto León, Carlos Segura, and Gara Miranda Universidad de La Laguna, Spain E-mail: {cleon, csegura, gmiranda}@ull.es José M. Chaves-González, Miguel A. Vega-Rodríguez, and Juan A. Gómez-Pulido Universidad de Extremadura, Spain E-mail: {jm, mavega, jangomez}@unex.es based algorithm (Scatter Search) and a trajectory based (1+1) Evolutionary Algorithm. Finally, the analysis of the frequency plans obtained offers insight about how the interference cost is reduced in the optimal plans.

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