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Exploiting Bivariate Dependencies to Speedup Structure Learning in Bayesian Optimization Algorithm

Journal of Computer Science & Technology 2012年第5期27卷 1077-1090页

作者： Amin Nikanjam adel rahmani School of Computer Engineering Iran University of Science and Technology

Bayesian optimization algorithm （BOA） is one of the successful and widely used estimation of distribution algorithms （EDAs） which have been employed to solve different optimization problems. In EDAs, a model is learned from the selected population that encodes interactions among problem variables. New individuals are generated by sampling the model and incorporated into the population. Different probabilistic models have been used in EDAs to learn interactions. Bayesian network （BN） is a well-known graphical model which is used in BOA. Learning a propel model in EDAs and particularly in BOA is distinguished as a computationally expensive task. Different methods have been proposed in the literature to improve the complexity of model building in EDAs. This paper employs bivariate dependencies to learn accurate BNs in BOA efficiently. The proposed approach extracts the bivariate dependencies using an appropriate pairwise interaction-detection metric. Due to the static structure of the underlying problems, these dependencies are used in each generation of BOA to learn an accurate network. By using this approach, the computational cost of model building is reduced dramatically. Various optimization problems are selected to be solved by the algorithm. The experimental results show that the proposed approach successfully finds the optimum in problems with different types of interactions efficiently. Significant speedups are observed in the model building procedure as well.

关键词： evolutionary computation Bayesian optimization algorithm Bayesian network model building bivariate interaction

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Towards automated software model checking using graph transformation systems and Bogor

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2009年第8期10卷 1093-1105页

作者： Vahid RAFE adel T.rahmani Department of Computer Engineering Iran University of Science and Technology

Graph transformation systems have become a general formal modeling language to describe many models in software development *** modeling of dynamic systems and model-to-model transformations are only a few examples in which graphs have been used to software *** even the perfect graph transformation system must be equipped with automated analysis capabilities to let users understand whether such a formal specification fulfills their *** this paper,we present a new solution to verify graph transformation systems using the Bogor model *** attributed graph grammars(AGG)-like graph transformation systems are translated to Bandera intermediate representation(BIR),the input language of Bogor,and Bogor verifies the model against some interesting properties defined by combining linear temporal logic(LTL) and special-purpose graph *** results are encouraging,showing that in most cases our solution improves existing approaches in terms of both performance and expressiveness.

关键词： Graph transformation Verification Bogor Attributed graph grammars (AGG) Software model checking

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