We described in this paper the use of Modular Neural Networks (MNN) for pattern recognition in parallel using a cluster of computers with a master-slave topology. In this paper, we are proposing the use of MNN to face...
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ISBN:
(纸本)9781424496365
We described in this paper the use of Modular Neural Networks (MNN) for pattern recognition in parallel using a cluster of computers with a master-slave topology. In this paper, we are proposing the use of MNN to face recognition with large databases to validate this approach. Also, a parallelgenetic algorithm to optimization architecture was used.
This paper deals with the resolution of the Quadratic 3-dimensional Assignment Problem hereafter referred to as Q3AP. Q3AP is an extension of the well-known Quadratic Assignment Problem (QAP) and of the Axial 3-Assign...
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This paper deals with the resolution of the Quadratic 3-dimensional Assignment Problem hereafter referred to as Q3AP. Q3AP is an extension of the well-known Quadratic Assignment Problem (QAP) and of the Axial 3-Assignment Problem (A3AP). It finds its application amongst others in Hybrid Automatic Repeat reQuest (HARQ) error-control mechanism used in wireless communication systems. This problem is computationally NP-hard. As far as we know, the largest Q3AP instance size solved to optimality is 13 whereas practical Q3AP instance size can be of 8, 16, 32 or 64. Sequential exact methods such branch-and-bound or sequential metaheuristics are therefore not suited to solve large size instances for the excessive needed computation time. In this paper, we propose parallel hybrid genetic-based metaheuristics for solving the Q3AP. The parallelism in our methods is of two hierarchical levels. The first level is an insular model where a fixed number of geneticalgorithms (GA) evolve independently on separate islands and periodically exchange genetic material. The second level is a parallel transformation of individuals in each GA. Implementation has been done using ParadisEO(a) framework, and the experiments have been performed on GRID5000, the French nation-wide computational grid. The experimental results produced by our method were confronted with those reported in the literature. The optimum or the best so far known solutions have been reached in a reasonable computation time.
Systematic testing of parallelgenetic algorithm defined on simple topologies such as ring, band, and fully connect graph are made with two benchmark problems, the 0-1 Knapsack Problem and the Weierstrass function. Wi...
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ISBN:
(纸本)9781424478354
Systematic testing of parallelgenetic algorithm defined on simple topologies such as ring, band, and fully connect graph are made with two benchmark problems, the 0-1 Knapsack Problem and the Weierstrass function. With fixed communication rule between sub-populations, our numerical results indicate that the band configuration is superior to other topologies tested. A heuristic understanding in terms of the compromise between exploitation and exploration in the solution space is suggested.
The Unequal Area Facility Layout Problem (UA-FLP) has been addressed by various methods, including mathematical modelling, heuristic and metaheuristic approaches. Nevertheless, each type of approach presents problems ...
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The Unequal Area Facility Layout Problem (UA-FLP) has been addressed by various methods, including mathematical modelling, heuristic and metaheuristic approaches. Nevertheless, each type of approach presents problems such as premature convergence, lack of diversity, or high computational cost. In this paper, for the first time, an Island Model genetic Algorithm (IMGA) is proposed to solve these subjects in the UA-FLP. The parallel evolution of several populations is used to maintain the population diversity and to obtain a wider sampling of the search space to obtain better quality solutions in fewer generations. Our novel approach was tested with a well-known set of problems taken from the literature and the results were compared with those of previous reports. In most cases, the results obtained by our novel approach improved on the previous results. Additionally, the proposed approach is able to reach good solutions with a wide range of problem sizes and in a reasonable computational time. (C) 2016 Elsevier Ltd. All rights reserved.
Understanding the regulation of gene expression is one of the key problems in current biology. A promising method for that purpose is the determination of the temporal dynamics between known initial and ending network...
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Understanding the regulation of gene expression is one of the key problems in current biology. A promising method for that purpose is the determination of the temporal dynamics between known initial and ending network states, by using simple acting rules. The huge amount of rule combinations and the nonlinear inherent nature of the problem make geneticalgorithms an excellent candidate for finding optimal solutions. As this is a computationally intensive problem that needs long runtimes in conventional architectures for realistic network sizes, it is fundamental to accelerate this task. In this article, we study how to develop efficient parallel implementations of this method for the fine-grained parallel architecture of graphics processing units (GPUs) using the compute unified device architecture (CUDA) platform. An exhaustive and methodical study of various parallelgenetic algorithm schemes-master-slave, island, cellular, and hybrid models, and various individual selection methods (roulette, elitist)-is carried out for this problem. Several procedures that optimize the use of the GPU's resources are presented. We conclude that the implementation that produces better results (both from the performance and the genetic algorithm fitness perspectives) is simulating a few thousands of individuals grouped in a few islands using elitist selection. This model comprises 2 mighty factors for discovering the best solutions: finding good individuals in a short number of generations, and introducing genetic diversity via a relatively frequent and numerous migration. As a result, we have even found the optimal solution for the analyzed gene regulatory network (GRN). In addition, a comparative study of the performance obtained by the different parallel implementations on GPU versus a sequential application on CPU is carried out. In our tests, a multifold speedup was obtained for our optimized parallel implementation of the method on medium class GPU over an equivalent sequenti
Attribute subset selection based on rough sets is a crucial preprocessing step in data mining and pattern recognition to reduce the modeling complexity. To cope with the new era of big data, new approaches need to be ...
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Attribute subset selection based on rough sets is a crucial preprocessing step in data mining and pattern recognition to reduce the modeling complexity. To cope with the new era of big data, new approaches need to be explored to address this problem effectively. In this paper, we review recent work related to attribute subset selection in decision-theoretic rough set models. We also introduce a scalable implementation of a parallelgenetic algorithm in Hadoop MapReduce to approximate the minimum reduct which has the same discernibility power as the original attribute set in the decision table. Then, we focus on intrusion detection in computer networks and apply the proposed approach on four datasets with varying characteristics. The results show that the proposed model can be a powerful tool to boost the performance of identifying attributes in the minimum reduct in large-scale decision systems. (C) 2016 Elsevier B.V. All rights reserved.
Security is an essential factor in wireless sensor networks especially for E-health applications. One of the common mechanisms to satisfy the security requirements is cryptography. Among the cryptographic methods, ell...
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ISBN:
(纸本)9781509020881
Security is an essential factor in wireless sensor networks especially for E-health applications. One of the common mechanisms to satisfy the security requirements is cryptography. Among the cryptographic methods, elliptic curve cryptography is well-known, as by having a small key length it provides the same security level in comparison with the other public key cryptosystems. The small key sizes make ECC very interesting for devices with limited processing power or memory such as wearable devices for E-health applications. It is vitally important that elliptic curves are protected against all kinds of attacks concerning the security of elliptic curve cryptography. Selection of a secure elliptic curve is a mathematically difficult problem. In this paper, an efficient elliptic curve selection framework, called SEECC, is proposed to select a secure and efficient curve front all the available elliptic curves. This method enhances the security and efficiency of elliptic curve cryptosystems by using a parallelgenetic algorithm.
Honeycomb sandwich structures are used in a wide variety of applications. Nevertheless, because of manufacturing defects or impact loads, these structures can experience imperfect bonding or debonding between the skin...
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Honeycomb sandwich structures are used in a wide variety of applications. Nevertheless, because of manufacturing defects or impact loads, these structures can experience imperfect bonding or debonding between the skin and the honeycomb core. Instances of debonding reduce the bending stiffness of the composite panel, which causes detectable changes in its vibration characteristics. This article presents a new methodology to identify debonded regions in aluminium honeycomb panels that uses an inverse algorithm based on parallel genetic algorithms. The honeycomb panels are modelled with finite elements using a simplified three-layer shell model. The adhesive layer between the skin and core is modelled using linear springs, with reduced rigidity for the debonded sectors. The algorithm is validated using experimental data from an aluminium honeycomb panel containing different damage scenarios. Copyright (c) 2015 John Wiley & Sons, Ltd.
Flexible manufacturing systems (FMS) aim at efficiently reacting to changing market needs to stand the increasing competitiveness. This imposes efficiency and flexibility requirements on FMS scheduling. Manufacturing ...
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ISBN:
(纸本)9781424452262
Flexible manufacturing systems (FMS) aim at efficiently reacting to changing market needs to stand the increasing competitiveness. This imposes efficiency and flexibility requirements on FMS scheduling. Manufacturing scheduling is the process of allocating available manufacturing resources to the set of planned jobs over time. It is an optimization process by which limited manufacturing resources are to be allocated to several jobs of different products efficiently. The agent-based scheduling approach has shown the ability to fulfill the flexibility requirement. Although this approach emphasizes flexibility, it lacks the optimization support. In this paper, an agent-based scheduling approach is extended with parallel genetic algorithms (PGA) to provide the required optimization support. Test results have shown a remarkable enhancement to the optimality of the generated schedules with respect to the predefined set of manufacturing objectives. The extended approach fulfils both flexibility and efficiency requirements on manufacturing scheduling.
This paper describes the application of our distributed computing framework for crystal structure prediction, Modified geneticalgorithms for Crystal and Cluster Prediction (MGAC), to predict the crystal structure of ...
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ISBN:
(纸本)9783642040696
This paper describes the application of our distributed computing framework for crystal structure prediction, Modified geneticalgorithms for Crystal and Cluster Prediction (MGAC), to predict the crystal structure of the two known polymorphs of bicalutamide. The paper describes our success in finding the lower energy polymorph and the difficulties encountered in finding the second one. The results show that geneticalgorithms are very effective in finding low energy crystal conformations, but unfortunately many of them are not plausible due to spurious effects introduced by the energy potential function used in the selection process. We propose to solve this by using a multi objective optimization GA approach, adding the unit cell volume as a second optimization target.
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