Multimodal optimization algorithms inspired by the immune system are generally characterized by a dynamic control of the population size and by diversity maintenance along the search. One of these proposals, denoted c...
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Multimodal optimization algorithms inspired by the immune system are generally characterized by a dynamic control of the population size and by diversity maintenance along the search. One of these proposals, denoted copt-aiNet (artificial immune network for combinatorial optimization), is used to deal with combinatorial problems like the Traveling Salesman Problem (TSP) and other permutation problems. In this paper, the copt-aiNet algorithm is extended and adapted to be applied to an important issue of modern data mining, the biclustering problem. The biclustering approach consists in simultaneously ordering the rows and columns of a given matrix, so that similar elements are grouped together. To illustrate the performance of the proposed method, two bitmap images are scrambled and used as input to the algorithm, and the biclustering procedure tries to restore the original image by grouping the pixels according to the similarity of colors in a neighborhood. Additionally, copt-aiNet is applied to gene expression data clustering, a classical problem of the bioinformatics literature, and its performance is compared with a hierarchical biclustering algorithm.
Multimodal optimization algorithms are being adapted to deal with dynamic optimization, mainly due to their ability to provide a faster reaction to unexpected changes in the optimization surface. The faster reaction m...
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Multimodal optimization algorithms are being adapted to deal with dynamic optimization, mainly due to their ability to provide a faster reaction to unexpected changes in the optimization surface. The faster reaction may be associated with the existence of two important attributes in population-based algorithms devoted to multimodal optimization: simultaneous maintenance of multiple local optima in the population; and self-regulation of the population size along the search. The optimization surface may be subject to variations motivated by one of two main reasons: modification of the objectives to be fulfilled and change in parameters of the problem. An immune-inspired algorithm specially designed to deal with combinatorial optimization is applied here to solve time-varying TSP instances, with the cost of going from one city to the other being a function of time. The proposal presents favorable results when compared to the results produced by a high-performance ant colony optimization algorithm of the literature.
Projection pursuit learning (PPL) refers to a well-known constructive learning algorithm characterized by a very efficient and accurate computational procedure oriented to nonparametric regression. It has been employe...
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Projection pursuit learning (PPL) refers to a well-known constructive learning algorithm characterized by a very efficient and accurate computational procedure oriented to nonparametric regression. It has been employed as a means to counteract some problems related to the design of artificial neural network (ANN) models, namely, the estimation of a (usually large) number of free parameters, the proper definition of the model's dimension, and the choice of the sources of nonlinearities (activation functions). In this work, the potentials of PPL are exploited through a different perspective, namely, in designing one-hidden-layer feedforward ANNs for the adaptive control of nonlinear dynamic systems. For such purpose, the proposed methodology is divided into three stages. In the first, the model identification process is undertaken. In the second, the ANN structure is defined according to an offline control setting. In these two stages, the PPL algorithm estimates not only the optimal number of hidden neurons but also the best activation function for each node. The final stage is performed online and promotes a fine-tuning in the parameters of the identification model and the controller. Simulation results indicate that it is possible to design effective neural models based on PPL for the control of nonlinear multivariate systems, with superior performance when compared to benchmarks
Support vector clustering (SVC) is a recently proposed clustering methodology with promising performance for high-dimensional and noisy datasets, and for clusters with arbitrary shape. This work addresses the applicat...
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Support vector clustering (SVC) is a recently proposed clustering methodology with promising performance for high-dimensional and noisy datasets, and for clusters with arbitrary shape. This work addresses the application of SVC, a kernel-based method, in a context in which the channel equalization problem is conceived as a clustering task. The main challenge, in this case, is to perform unsupervised clustering aiming at the design of an optimal Bayesian or a blind prediction-based receiver without resorting to a priori information about the transmission medium. The proposed technique employs a two-stage procedure -a combination between the use of SVC to obtain a first set of clusters and an auxiliary heuristic to help separating eventual multiple clouds contained in a single cluster and attribute centers to them via an iterated local search (ILS) algorithm. The obtained results indicate that kernel methods can be successfully applied to the field of signal processing.
In this work, we propose and analyze the applicability of a novel unsupervised data clustering technique in the problem of channel equalization. The proposal combines two different methods, a neuro-immune network call...
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In this work, we propose and analyze the applicability of a novel unsupervised data clustering technique in the problem of channel equalization. The proposal combines two different methods, a neuro-immune network called RABNET [1] and the iterated local search algorithm (ILS) [2], to produce a tool that, in contrast to classical solutions like the k-means algorithm, does not require a priori knowledge about the number of clusters to be found and, moreover, possesses mechanisms to avoid local convergence. Simulation results attest both the viability and efficiency of the proposal in scenarios conceived to highlight certain aspects that can be decisive insofar as real-world applications are concerned.
In this work, we propose an evolutionary-like approach to the problem of blind adaptive spatial filtering that is based on the decision-directed criterion and on the dopt-aiNet, an artificial immune network conceived ...
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In this work, we propose an evolutionary-like approach to the problem of blind adaptive spatial filtering that is based on the decision-directed criterion and on the dopt-aiNet, an artificial immune network conceived to perform multimodal search in dynamic environments. The proposal was tested under static and time-varying undermodeled channel models, and, in all cases, its ability to find and track a solution close to the Wiener global optimum was attested. The obtained results reveal that the dopt-aiNet may decisively enhance the performance of adaptive arrays in scenarios built from elements that are representative of some aspects of real-world communication systems.
One of the main problems related to regulatory network reconstruction from expression data concerns the small size and low quality of the available dataset. When trying to infer a model from little information it is n...
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One of the main problems related to regulatory network reconstruction from expression data concerns the small size and low quality of the available dataset. When trying to infer a model from little information it is necessary to give much more precedence to generalization, rather than specificity, otherwise, any attempt will be fated to overfitting. In this paper we address this issue by focusing on data sparseness and noisy information, and propose a density estimation technique that achieves regularized curves when data is scarce. We first compare the proposed method with the EM algorithm for mixture models on density estimation problems. Next, we apply the method, together with Bayesian networks, on realistic simulations of static gene networks, and compare the obtained results with the standard discrete Bayesian network model. We intend to demonstrate that adopting a discrete approach is not justifiable when only a small amount of information is available.
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