International audienceAssociation rules mining has been largely studied by the data mining community. ARM aims to extract the interesting rules from any given transactional database. This problem is well known to be time consuming in general. This paper deals with association rules mining algorithms to cope with very large databases and especially for those existing on the web. Many polynomial exact algorithms already proposed in literature have shown their efficiency when dealing with small and medium datasets. Unfortunately, their efficiency is not enough for handling the huge amount of data in the web context requiring a real time response. Not surprisingly, some bio-inspired methods seem to be clearly more appropriate. This paper mainly proposes a new ARM algorithm based on an improved version of bees swarm optimisation with three different heuristics for exploring the search area. This approach has been implemented and experimented on different dataset benchmarks with small size, medium size and large size. These first empirical results highlighted that our approach outperforms some other existing algorithms both in terms of fitness criterion and CPU time

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In this paper, we propose a memetic algorithm for the optimal winner determination problem in combinatorial auctions. First, we investigate a new selection strategy based on both fitness and diversity to choose individuals to participate in the reproduction phase of the memetic algorithm. The resulting algorithm is enhanced by using a stochastic local search (SLS) component combined with a specific crossover operator. This operator is used to identify promising search regions while the stochastic local search performs an intensified search of solutions around these regions. Experiments on various realistic instances of the considered problem are performed to show and compare the effectiveness of our approach.

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