35739456
A Fast Learning Algorithm Based on Layered Hessian Approximations and the Pseudoinverse
In this article, we present a simple, effective method to learning for an MLP that is based on approximating the Hessian using only local information, specifically, the correlations of output activations from previous layers of hidden neurons. This approach of training the hidden layer weights with the Hessian approximation combined with the training of the final output layer of weights using the pseudoinverse [1] yields improved performance at a fraction of the computational and structural complexity of conventional learning algorithms.
35157
A cooperative coevolutionary algorithm for multiobjective optimization
This paper presents a kind of cooperative coevolutionary algorithm (CCEA) for multi-objective optimization (MOO). In this algorithm, solutions evolve in the form of cooperative subpopulations. An archive stores non-dominated solutions and helps to evaluate individuals in the subpopulations. The mechanism of niching is applied to maintain the diversity of solutions in the archive. Meanwhile, an extending operator is designed to mine information on solution distribution from the archive and guide the search to regions that are not explored enough. Extensive simulations are performed on different benchmark problems for various multi-objective evolutionary algorithms (MOEAs) and indicate that CCEA is strongly competitive with five recent well-known MOEAs in finding a good non-dominated solution set.
23752904
Solving multiobjective vehicle routing problem with stochastic demand via evolutionary computation
Abstract This paper considers the routing of vehicles with limited capacity from a central depot to a set of geographically dispersed customers where actual demand is revealed only when the vehicle arrives at the customer. The solution to this vehicle routing problem with stochastic demand (VRPSD) involves the optimization of complete routing schedules with minimum travel distance, driver remuneration, and number of vehicles, subject to a number of constraints such as time windows and vehicle capacity. To solve such a multiobjective and multi-modal combinatorial optimization problem, this paper presents a multiobjective evolutionary algorithm that incorporates two VRPSD-specific heuristics for local exploitation and a route simulation method to evaluate the fitness of solutions. A new way of assessing the quality of solutions to the VRPSD on top of comparing their expected costs is also proposed. It is shown that the algorithm is capable of finding useful tradeoff solutions for the VRPSD and the solutions are robust to the stochastic nature of the problem. The developed algorithm is further validated on a few VRPSD instances adapted from Solomon’s vehicle routing problem with time windows (VRPTW) benchmark problems.
5733190
Automating the drug scheduling of cancer chemotherapy via evolutionary computation
This paper presents the optimal control of drug scheduling in cancer chemotherapy using a distributed evolutionary computing software. Unlike conventional methods that often require gradient information or hybridization of different approaches in drug scheduling, the proposed evolutionary optimization methodology is simple and capable of automatically finding the near-optimal solutions for complex cancer chemotherapy problems. It is shown that different number of variable pairs in evolutionary representation for drug scheduling can be easily implemented via the software, since the computational workload is shared and distributed among multiple computers over the Internet. Simulation results show that the proposed evolutionary approach produces excellent control of drug scheduling in cancer chemotherapy, which are competitive or equivalent to the best solutions published in literature.
15535824
Adapting evolutionary dynamics of variation for multi-objective optimization
Many real-world applications involve complex optimization problem with various competing specifications and constraints that are often difficult, if not impossible, to be solved without the aid of powerful and efficient optimization algorithms. Although evolutionary algorithms have proven to be successful with respect to the optimization goals of proximity and diversity, their capability is bottlenecked by the evolutionary operators' abilities to deal with the complicated search spaces. Furthermore, it is well known that the algorithm's performances in different problems are sensitive to the parameter setting of the operators. In an effort to adapt the evolutionary search ability along the different regions of the search space, this paper proposes a dynamic variation operator whose parameter value is deterministically adapted during the algorithm run so as to maintain a balance between the extensive exploration in the early phase and local fine-tuning in the end phase. Comparative studies with some representative variation operators are performed on different benchmark problems to illustrate the effectiveness and efficiency of the proposed operator.
5670523
The Intelligent Alarm Management System
Nuisance alarms often clutter and obscure a process plant operator's view of critical information, with potentially severe consequences. The intelligent alarm management system (IAMS) suppresses nuisance alarms and provides valuable advisory information to help the operator focus quickly on important alarm information and take correct, quick actions. Test results show that IAMS effectively suppresses nuisance alarms, making alarm systems more helpful.
10451148
Discontinuity preserving stereo with small baseline multi-flash illumination
Currently, sharp discontinuities in depth and partial occlusions in multiview imaging systems pose serious challenges for many dense correspondence algorithms. However, it is important for 3D reconstruction methods to preserve depth edges as they correspond to important shape features like silhouettes which are critical for understanding the structure of a scene. In this paper, we show how active illumination algorithms can produce a rich set of feature maps that are useful in dense 3D reconstruction. We start by showing a method to compute a qualitative depth map from a single camera, which encodes object relative distances and can be used as a prior for stereo. In a multiview setup, we show that along with depth edges, binocular half-occluded pixels can also be explicitly and reliably labeled. To demonstrate the usefulness of these feature maps, we show how they can be used in two different algorithms for dense stereo correspondence. Our experimental results show that our enhanced stereo algorithms are able to extract high quality, discontinuity preserving correspondence maps from scenes that are extremely challenging for conventional stereo methods.
5075023
Selecting Objects With Freehand Sketches
We present in this paper the design of an interactive tool for selecting objects using simple freehand sketches. The objective is to extract object boundaries precisely while requiring little skill and time from the user. The tool proposed achieves this objective by integrating user input and image computation in a two-phase algorithm. In the first phase, the input sketch is used along with a coarse global segmentation of the image to derive an initial selection and a triangulation of the region around the boundary. The triangles are used to formulate subproblems of local finer-grained segmentation and selection. Each of the subproblems is processed independently in the second phase, where a linear approximation of the local boundary as well as a local, finer-grained segmentation are computed. The approximate boundary is then used with the local segmentation to compute a final selection, represented with an alpha channel to f . l l y capture diffiised object boundaries. Experimental results show that the tool allows veiy simple sketches to be used to select objects with complex boundaries. Therefore, the tool has immediate applications in graphics systems for image editing, manipdation, synthesis, retrieval, and processing.
11570061
Graphical Composition of Grid Services
Grid services and web services have similarities but also significant differences. Although conceived for web services, it is seen how BPEL (Business Process Execution Logic) can be used to orchestrate a collection of grid services. It is explained how CRESS (Chisel Representation Employing Systematic Specification) has been extended to describe grid service composition. The CRESS descriptions are automatically converted into BPEL/WSDL code for practical realisation of the composed services. This achieves orchestration of grid services deployed using the widely used Globus Toolkit and ActiveBPEL interpreter. The same CRESS descriptions are automatically translated into LOTOS, allowing systematic checks for interoperability and logical errors prior to implementation.
1118547
Appearance-based eye gaze estimation
We present a method for estimating eye gaze direction, which represents a departure from conventional eye gaze estimation methods, the majority of which are based on tracking specific optical phenomena like corneal reflection and the Purkinje images. We employ an appearance manifold model, but instead of using a densely sampled spline to perform the nearest manifold point query, we retain the original set of sparse appearance samples and use linear interpolation among a small subset of samples to approximate the nearest manifold point. The advantage of this approach is that since we are only storing a sparse set of samples, each sample can be a high dimensional vector that retains more representational accuracy than short vectors produced with dimensionality reduction methods. The algorithm was tested with a set of eye images labelled with ground truth point-of-regard coordinates. We have found that the algorithm is capable of estimating eye gaze with a mean angular error of 0.38 degrees, which is comparable to that obtained by commercially available eye trackers.
6776829
Semi-supervised Kernel-Based Fuzzy C-Means
This paper presents a semi-supervised kernel-based fuzzy c-means algorithm called S 2 KFCM by introducing semi-supervised learning technique and the kernel method simultaneously into conventional fuzzy clustering algo- rithm. Through using labeled and unlabeled data together, S 2 KFCM can be ap- plied to both clustering and classification tasks. However, only the latter is con- cerned in this paper. Experimental results show that S 2 KFCM can improve classification accuracy significantly, compared with conventional classifiers trained with a small number of labeled data only. Also, it outperforms a similar approach S 2 FCM.