This paper presents a new approach to robustness analysis in multi-objective optimization problems aimed at obtaining the most robust Pareto front solutions and distributing the solutions along the most robust regions of the optimal Pareto set. A new set of test problems accounting for the different types of robustness cases is presented in this study. Non-dominated solutions are classified according to their degree of robustness and are distributed along the Pareto front according to specific algorithm parameter values. Verification of the proposed method is carried out using the developed test problems and artificial and real world benchmark test problems present in the literature.

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].

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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.

Computational intelligence competitions have recently gained a lot of interest. These contests motivate and encourage researchers to participate on them, and to apply their work areas to specific games. During the last two years, one of the most popular competitions held on Computational Intelligence in Games conferences is the Car Racing Competition. This competition combines the fun of driving to win and the challenge of obtaining autonomous driving, which is known as a very difficult problem and faced by a lot of researches from different perspectives. For this competition, we have developed a controller with fuzzy rules and fuzzy sets for input and output, which were evolved using a genetic algorithm in order to optimise lap times, damage taken and out of track time. The design of this controller is explained in detail in this article, as well as the results obtained at the end of the contest.