5502812
A Continuous Genetic Algorithm for Global Optimization
40205984
A Robust Evolutionary Algorithm for Training Neural Networks
A new evolutionary algorithm is introduced for training both feedforward and recurrent neural networks. The proposed approach, called the Family Competition Evolutionary Algorithm (FCEA), automatically achieves the balance of the solution quality and convergence speed by integrating multiple mutations, family competition and adaptive rules. We experimentally analyse the proposed approach by showing that its components can cooperate with one another, and possess good local and global properties. Following the description of implementation details, our approach is then applied to several benchmark problems, including an artificial ant problem, parity problems and a two-spiral problem. Experimental results indicate that the new approach is able to stably solve these problems, and is very competitive with the comparative evolutionary algorithms.
207143686
Estimating The Attribute Weights Through Evidential Reasoning And Mathematical Programming
An approach for estimating attribute weight is proposed, which is addressed to the problem inverse to the aggregation assessment based on a hierarchy of attributes. Evidential reasoning framework is employed since the assessment on some basic attributes may be uncertain and incomplete. As a by-product, a simple formula for evidence combination for our special case is obtained. The weights are estimated based on the preference given by the decision-maker, and represented as intervals. The more abundant preference the decision-maker shows, the clearer the relation among the basic attributes.
19364030
The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees
Abstract Multiple attribute decision analysis (MADA) problems having both quantitative and qualitative attributes under uncertainty can be modeled using the evidential reasoning (ER) approach. Several types of uncertainties such as ignorance and fuzziness can be modeled in the ER framework. In this paper, the ER approach will be extended to model new types of uncertainties including interval belief degrees and interval data that could be incurred in decision situations such as group decision making. The Dempster–Shafer (D–S) theory of evidence is first extended, which is one of the bases of the ER approach. The analytical ER algorithm is used to combine all evidence simultaneously. Two pairs of nonlinear optimization models are constructed to estimate the upper and lower bounds of the combined belief degrees and to compute the maximum and the minimum expected utilities of each alternative, respectively. Interval data are equivalently transformed to interval belief degrees and are incorporated into the nonlinear optimization models. A cargo ship selection problem is examined to show the implementation process of the proposed approach.
388734
A statistical signal processing approach to image fusion for concealed weapon detection
A statistical signal processing approach to multisensor image fusion is presented for concealed weapon detection (CWD). This approach is based on an image formation model in which the sensor images are described as the true scene corrupted by additive non-Gaussian distortion. The expectation-maximization (EM) algorithm is used to estimate the model parameters and the fused image. We demonstrate the efficiency of this approach by applying this method to fusion of visual and non-visual images with emphasis on CWD applications.
17411195
Offer Group Generation and Delayed Processing in Multi-Issue Negotiation
In this paper we describe a mechanism for automated negotiation in which artificial agents can deal with a multi-issue negotiation in a one-to-many way based on some strategies. The negotiation process are governed dynamically by some strategies that emphasize on the diverse processing methods under the considering of different commodity attributes, including dynamic delayed evaluation and response to the received offers, generation of offer group with the same utility. Our analysis shows that the mechanism ensures that bargaining agents could reach a better agreement automatically. We also implement a prototype system to verify the advantages of our mechanism.
5924956
On joining and caching stochastic streams
We consider the problem of joining data streams using limited cache memory, with the goal of producing as many result tuples as possible from the cache. Many cache replacement heuristics have been proposed in the past. Their performance often relies on implicit assumptions about the input streams, e.g., that the join attribute values follow a relatively stationary distribution. However, in general and in practice, streams often exhibit more complex behaviors, such as increasing trends and random walks, rendering these "hardwired" heuristics inadequate.In this paper, we propose a framework that is able to exploit known or observed statistical properties of input streams to make cache replacement decisions aimed at maximizing the expected number of result tuples. To illustrate the complexity of the solution space, we show that even an algorithm that considers, at every time step, all possible sequences of future replacement decisions may not be optimal. We then identify a condition between two candidate tuples under which an optimal algorithm would always choose one tuple over the other to replace. We develop a heuristic that behaves consistently with an optimal algorithm whenever this condition is satisfied. We show through experiments that our heuristic outperforms previous ones.As another evidence of the generality of our framework, we show that the classic caching/paging problem for static objects can be reduced to a stream join problem and analyzed under our framework, yielding results that agree with or extend classic ones.