International Conference Papers

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Author: search.filters.author.Benatchba, Karima

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    Impact of Genetic Algorithms Operators on Association Rules Extraction
    (2016-11-11) Hamdad, Leila; Ournani, Zakaria; Benatchba, Karima; Bendjoudi, Ahcène
    In this paper we study the impact of GAs’ components such as encoding, different crossover, mutation and replacement strategies on the number of extracted association rules and their quality. Moreover, we propose a strategy to manage the population. The later is organized in classes where each one encloses same size rules. Each class can be seen as a population on which a GA is applied. All tests are conducted on two types of benchmarks : synthetic and real ones of different sizes.
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    OVERHEARING IN FINANCIAL MARKETS: A Multi-agent Approach
    (SciTePress Science and Technology Publications, 2011) Hedjazi Dellal, Badiâa; Aknine, Samir; Ahmed-Nacer, Mohamed; Benatchba, Karima
    Open complex systems as financial markets evolve in a highly dynamic and uncertain environment. They are often subject to significant fluctuations due to unanticipated behaviours and information. Modelling and simulating these systems by means of agent systems, i.e., through artificial markets is a valuable approach. In this article, we present our model of asynchronous artificial market consisting of a set of adaptive and heterogeneous agents in interaction. These agents represent the various market participants (investors and institutions). Investor Agents have advanced mental models for ordinary investors which do not relay on fundamental or technical analysis methods. On one hand, these models are based on the risk tolerance and on the other hand on the information gathered by the agents. This information results from overhearing influential investors in the market or the order books. We model the system through investor agents using learning classifier systems as reasoning models. As a result, our artificial market allows the study of overhearing impacts on the market. We also present the experimental evaluation results of our model.
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    Parallel B&B Algorithm on Hybrid Multicore/GPU Architecture
    (IEEE, 2013-11-15) Bendjoudi, Ahcène; Chekini, Mehdi; Gharbi, Makhlouf; Mehdi, Malika; Benatchba, Karima; Sitayeb-Benbouzid, Fatima; Melab, Nouredine
    B&B algorithms are well known techniques for exact solving of combinatorial optimization problems (COP). They perform an implicit enumeration of the search space instead of exhaustive one. Based on a pruning technique, they reduce considerably the computation time required to explore the whole search space. Nevertheless, these algorithms remain inefficient when dealing with large combinatorial optimization instances. They are time-intensive and they require a huge computing power to be solved optimally. Nowadays, multi-core-based processors and GPU accelerators are often coupled together to achieve impressive performances. However, classical B&B algorithms must be rethought to deal with their two divergent architectures. In this paper, we propose a new B&B approach exploiting both the multi-core aspect of actual processors and GPU accelerators. The proposed approaches have been executed to solve FSP instances that are well-known combinatorial optimization benchmarks. Real experiments have been carried out on an Intel Xeon 64-bit quad-core processor E5520 coupled to an Nvidia Tesla C2075 GPU device. The results show that our hybrid B&B approach speeds up the execution time up to x123 over the sequential mono-core B&B algorithm.