In a genetic algorithm (GA), the undesirable phenomenon of excess convergence can often occur. Excess convergence is the phenomenon where the diversity of a group is lost. This phenomenon occurs because homogeneous in...
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In a genetic algorithm (GA), the undesirable phenomenon of excess convergence can often occur. Excess convergence is the phenomenon where the diversity of a group is lost. This phenomenon occurs because homogeneous individuals are increased rapidly in the group while evolving or searching. Therefore, crossover loses its function. Once the excess convergence occurs, the search by the GA becomes meaningless. Therefore, it is important to avoid excess convergence and maintain diversity. First, we show an implementation of a parallel GA based on a multiple-group-type island model, that uses object-shared space. Next, as a simple, effective method for avoiding excess convergence, we propose a diversity maintenance technique based on selection of the homogeneous individuals called the Noah's ark strategy for parallel GAs, and demonstrate its effectiveness on a knapsack problem. Our proposed method is to replace individuals in sub-groups that have excessively converged with the new individuals coming from the search space. That is, we avoid excess convergence by expelling homogeneous individuals, with the exception of one "elite" individual (that we call for Noah). Thus, we limit a decrease in diversity of an entire group.
In a genetic algorithm(GA),the undesirable phenomenon of excess convergence can often occur. Excess convergence is the phenomenon where the diversity of a group is *** phenomenon occurs because homogeneous individuals...
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In a genetic algorithm(GA),the undesirable phenomenon of excess convergence can often occur. Excess convergence is the phenomenon where the diversity of a group is *** phenomenon occurs because homogeneous individuals are increased rapidly in the group while evolving or ***, crossover loses its *** the excess convergence occurs,the search by the GA becomes meaningless. Therefore,it is important to avoid excess convergence and maintain ***,we show an implementation of a parallel GA based on a multiple-group-type island model, that uses object-shared ***,as a simple,effective method for avoiding excess convergence,we propose a diversity maintenance technique based on selection of the homogeneous individuals called the Noah's ark strategy for parallel GAs,and demonstrate its effectiveness on a knapsack *** proposed method is to replace individuals in sub-groups that have excessively converged with the new individuals coming from the search space. That is,we avoid excess convergence by expelling homogeneous individuals,with the exception of one "elite" individual(that we call for Noah).Thus,we limit a decrease in diversity of an entire group.
The Level 1 Muon Trigger subsystem for BTeV will be implemented using the same architectural building blocks as the BTeV Level 1 Pixel Trigger: pipelined field programmable gate arrays feeding a farm of dedicated proc...
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The Level 1 Muon Trigger subsystem for BTeV will be implemented using the same architectural building blocks as the BTeV Level 1 Pixel Trigger: pipelined field programmable gate arrays feeding a farm of dedicated processing elements. The muon trigger algorithm identifies candidate tracks, and is sensitive to the muon charge (sign);candidate dimuon events are identified by complementary charge track-pairs. To insure that the trigger is operating effectively, the trigger development team is actively collaborating in an independent multi-university research program for reliable, self-aware, fault adaptive behavior in real-time embedded systems (RTES). Key elements of the architecture, algorithm, performance, and engineered reliability are presented.
A type of network called the Contender Network (CN) was earlier proposed by Ng, Erdogan and Ng (1995). A classification algorithm is used to assign weighted vote in a monotonically decreasing function of the rank in C...
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This work develops a model to calculate the radionuclides release from a repository for high level radioactive waste, taking into account multiple-canister interface. Once the overpack loses its integrity, the waste g...
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ISBN:
(纸本)9784888982566
This work develops a model to calculate the radionuclides release from a repository for high level radioactive waste, taking into account multiple-canister interface. Once the overpack loses its integrity, the waste glass starts to dissolve by porewater in the bentonite buffer. Bentonite is expected to have hydraulic conductivity more than three orders of magnitude less than that of the surrounding rocks. The migrating nuclide from the buffer region is transported in the near field granite host rock, then releases to the far field of the repository. A mass concentration calculation in the far field of the repository is also included in the model. The model is diffusion-advection model. The model is solved using wavelet Galerkin method (WGM). The model is devised to be fast and compact due to the compactly supported property of the Daubechies' wavelet. Since the scaling functions are compactly supported only a finite number of the connection coefficients are nonzero. The resultant matrix has block diagonal structure, which can be inverted easily. We tested our model for a try of canisters contains 200 canisters. The results show well agreements with the results obtained from the analytic solution with a proper selection of wavelet-dilation order pairs.
In this paper we propose a new hierarchical 3D facial model based on anatomical knowledge that provides high fidelity for realistic facial expression animation. Like real human face, the facial model has a hierarchica...
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In this paper we propose a new hierarchical 3D facial model based on anatomical knowledge that provides high fidelity for realistic facial expression animation. Like real human face, the facial model has a hierarchical biomechanical structure, incorporating a physically-based approximation to facial skin tissue, a set of anatomically-motivated facial muscle actuators and underlying skull structure. The deformable skin model has multi-layer structure to approximate different types of soft tissue. It takes into account the nonlinear stress-strain relationship of the skin and the fact that soft tissue is almost incompressible. Different types of muscle models have been developed to simulate distribution of the muscle force on the skin due to muscle contraction. By the presence of the skull model, our facial model takes advantage of both more accurate facial deformation and the consideration of facial anatomy during the interactive definition of facial muscles. Under the muscular force, the deformation of the facial skin is evaluated using numerical integration of the governing dynamic equations. The dynamic facial animation algorithm runs at interactive rate with flexible and realistic facial expressions to be generated.
A type of network called the Contender Network (CN) was earlier proposed by Ng, Erdogan and Ng (1995). A classification algorithm is used to assign weighted vote in a monotonically decreasing function of the rank in C...
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ISBN:
(纸本)9810475241
A type of network called the Contender Network (CN) was earlier proposed by Ng, Erdogan and Ng (1995). A classification algorithm is used to assign weighted vote in a monotonically decreasing function of the rank in CN. Modification to the CN classification algorithm known as the conscience algorithm is presented. However, a new problem is encountered when the conscience algorithm is used in CN. We name this problem as saturation problem (i.e. when saturation stage of the neural network is reached). This saturation problem is solved by introducing a count threshold. The threshold is decided rigorously through many experiments based on the criteria of the accuracy, error and confusion rates of the network performance. We present experiments that show that conscience algorithm introduced during training with appropriate count threshold can improve the network performance. Experimental results of this approach are presented and discussed through the application of the neural network in digit classification.
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