13961662
Improving Nearest Neighbor Classifier Using Tabu Search and Ensemble Distance Metrics
The nearest-neighbor (NN) classifier has long been used in pattern recognition, exploratory data analysis, and data mining problems. A vital consideration in obtaining good results with this technique is the choice of distance function, and correspondingly which features to consider when computing distances between samples. In this paper, a new ensemble technique is proposed to improve the performance of NN classifier. The proposed approach combines multiple NN classifiers, where each classifier uses a different distance function and potentially a different set of features (feature vector). These feature vectors are determined for each distance metric using Simple Voting Scheme incorporated in Tabu Search (TS). The proposed ensemble classifier with different distance metrics and different feature vectors (TS-DF/NN) is evaluated using various benchmark data sets from UCI Machine Learning Repository. Results have indicated a significant increase in the performance when compared with various well-known classifiers. Furthermore, the proposed ensemble method is also compared with ensemble classifier using different distance metrics but with same feature vector (with or without Feature Selection (FS)).
17447930
Incorporation of adaptive mutation based on subjective evaluation in an interactive evolution strategy
A rapidly emerging model in the field of adaptive computing is the symbiosis of human expertise with evolutionary algorithms for user controlled and directed search. The two aspects in any EA are the selection of individuals to reproduce based on some measure of their quality or fitness and the application of variation operators to produce new solutions. In the context of interactive evolution, these aspects are compounded by the need for rapid convergence to prevent user fatigue and to provide the user some control over the generation of new solutions. Elsewhere, in the work of the authors (2004), we have examined different policies for best incorporating the user into the evaluation and selection process. In this paper, we explore the hypothesis that user assigned fitness represents a source of information that can be used to control the variation process: effectively to broaden the search if none of the current solutions is promising, or focus the search and improve convergence speed in the vicinity of a good solution. The main aims of this study, therefore, are to analyse the advantages of using a user directed adaptive mutation strategy over fixed mutation step sizes in terms of time to converge and robustness of the resulting solution. We present results showing a qualitatively different type of search process can be obtained by using the user assigned fitness to control the nature of the mutation process. There is also a synergy between user-based selection and fitness-based mutation control which out performs either system on its own.
16664439
An alternative approach for the evaluation of the neocognitron
The neocognitron network is analysed from the point of view of the contribution of the di erent layers to the nal classi cation. A variation to the neocognitron which gives improved performance is suggested. This variant combines the low level feature extraction capabilities of the initial layers with alternative classi ers such as LVQ and Class Based Means Clustering. This is shown to give performance which is superior to the either of those classi ers acting on their own, and to the neocognitron in its standard form on two di erent instances of the letter recognition problem.
796697
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
The combination of evolutionary algorithms with local search was named "memetic algorithms" (MAs) (Moscato, 1989). These methods are inspired by models of natural systems that combine the evolutionary adaptation of a population with individual learning within the lifetimes of its members. Additionally, MAs are inspired by Richard Dawkin's concept of a meme, which represents a unit of cultural evolution that can exhibit local refinement (Dawkins, 1976). In the case of MA's, "memes" refer to the strategies (e.g., local refinement, perturbation, or constructive methods, etc.) that are employed to improve individuals. In this paper, we review some works on the application of MAs to well-known combinatorial optimization problems, and place them in a framework defined by a general syntactic model. This model provides us with a classification scheme based on a computable index D, which facilitates algorithmic comparisons and suggests areas for future research. Also, by having an abstract model for this class of metaheuristics, it is possible to explore their design space and better understand their behavior from a theoretical standpoint. We illustrate the theoretical and practical relevance of this model and taxonomy for MAs in the context of a discussion of important design issues that must be addressed to produce effective and efficient MAs.
17502763
Alignment Of Protein Structures With A Memetic Evolutionary Algorithm
CATEGORY: Real-World Applications 
 
Structural comparison of proteins is a core problem in modern biomedical research. Identifying structural similarities is essential for the assessment of the relationship between structure and function in proteins, and structural comparison techniques play a key role in applications like rational drug design. In this paper we consider a technique for protein structure comparison known as the maximum contact map overlap problem. In this problem, the similarity between two protein structures is computed by aligning the proteins to maximize the number of shared contacts in their corresponding contact maps. 
 
We present a new approach to this problem that uses a Multimeme evolutionary algorithm. The best solution found by our algorithm provides a lower bound on the value of the optimal structural alignment between the proteins. We have evaluated the Multimeme algorithm on a range of benchmark problems and compared with previous heuristics. We apply a linear programming method, which provides an upper bound, to assess the accuracy of our solutions. Our experiments show that the Multimeme evolutionary algorithm represents a significant improvement on the current state of the art in metaheuristics for this problem.
16182080
Self adaptation of mutation rates in a steady state genetic algorithm
This paper investigates the use of genetically encoded mutation rates within a "steady state" genetic algorithm in order to provide a self-adapting mutation mechanism for incremental evolution. One of the outcomes of this work will be a reduction in the number of parameters required to be set by the operator, thus facilitating the transfer of evolutionary computing techniques into an industrial setting. The NK family of landscapes is used to provide a variety of different problems with known statistical features in order to examine the effects of changing various parameters on the performance of the search. A number of policies are considered for the replacement of members of the population with newly created individuals and recombination of material between parents, and a number of methods of encoding for mutation rate are investigated. Empirical comparisons (using the "best-of current-population" metric) over a range of test problems show that a genetic algorithm incorporating the best "flavour" of the adaptive mutation operator outperformed the same algorithm when using any one of a variety of "standard" fixed mutation rates suggested by other authors.
12656940
Towards Semantic Universal Multimedia Access
14877105
Scalable multimedia delivery for pervasive computing
Growing numbers of pervasive devices are gaining access to the Internet and other information sources. However, much of the rich multimedia content cannot be easily handled by the client devices with limited communication, processing, storage and display capabilities. In order to improve access, we are developing a system for scalable delivery of multimedia. The system uses an InfoPyramid for managing and manipulating multimedia content composed of video, images, audio and text. The InfoPyramid manages the different variations of media objects with different fidelities and modalities and generates and selects among the alternatives in order to adapt the delivery to different client devices. We describe a system for scalable multimedia delivery for a variety of client devices, including PDAs, HHCs, smart phones, TV browsers and color PCs.
1926668
Exploring "Multiple Worlds" in Parallel
We examine computing alternative solutions to a problem in parallel to improve response time.Problems with exploring multiple alternatives in parallel include (1) side-effects and (2) combinatorial ...
41462793
S-STIR: similarity search through iterative refinement
Similarity retrieval of images based on texture and color features has generated a lot of interests recently. Most of these similarity retrievals are based on the computation of the Euclidean distance between the target feature vector and the feature vectors in the database. Euclidean distance, however, does not necessarily reflect either relative similarity required by the user. In this paper, a method based on nonlinear multidimensional scaling is proposed to provide a mechanism for the user to dynamically adjust the similarity measure. The results show that a significant improvement on the precision versus recall curve has been achieved.
3251263
Semantic video clustering across sources using bipartite spectral clustering
Data clustering is an important technique for visual data management. Most previous work focuses on clustering video data within single sources. We address the problem of clustering across sources, and propose novel spectral clustering algorithms for multisource clustering problems. Spectral clustering is a new discriminative method realizing clustering by partitioning data graphs. We represent multi-source data as bipartite or K-partite graphs, and investigate the spectral clustering algorithm under these representations. The algorithms are evaluated using the TRECVID-2003 corpus with semantic features extracted from speech transcripts and visual concept recognition results from videos. The experiments show that the proposed bipartite clustering algorithm significantly outperforms the regular spectral clustering algorithm in capturing cross-source associations.