Blind search for optimal Wiener equalizers using an artificial immune network model

This work proposes a framework to determine the optimal Wiener equalizer by using an artificial immune network model together with the constant modulus (CM) cost function. This study was primarily motivated by recent theoretical results concerning the CM criterion and its relation to the Wiener approach. The proposed immune-based technique was tested under different channel models and filter orders, and benchmarked against a procedure using a genetic algorithm with niching. The results demonstrated that the proposed strategy has a clear superiority when compared with the more traditional technique. The proposed algorithm presents interesting features from the perspective of multimodal search, being capable of determining the optimal Wiener equalizer in most runs for all tested channels.2003874074

Theoretical approaches to forensic entomology: II. Mathematical model of larval development

The present study represents the sequence of an overall theoretical approach to model phenomena of interest for forensic entomology. In particular in this paper, a mathematical model that describes the larval development is advanced for blowflies, which are usually the first to find a decomposing corpse. Data on development times for stages and instars of blowflies in experimental conditions have become the baseline information often used to estimate the age of maggots by interpolating these data against on-site conditions. This information on larval development is relevant to estimates of post-mortem interval (PMI) in forensic investigations.122527527

Larval competition for patchy resources in Chrysomya megacephala (Dipt., Calliphoridae): implications of the spatial distribution of immatures

In the present study, a single procedure was established to investigate the effect of the spatial distribution of immatures in patchy resources, on the outcome of larval competition for food. in experimental populations of Chrysomya megacephala. A theoretical model of intraspecific competition was extended and applied to experimental data on survival to adulthood for 20 larval densities, to obtain the theoretical mean number of individuals that will survive, considering a hypothetical previous random adult oviposition in a system of homogeneous patches. The survival curve obtained suggests that the larval competition for food in C. megacephala is of the scramble/exploitative type, which corroborates results from previous studies, although the latter did not consider the correlation between local and global abundances. The present model allows that experimental data could be perfectly applicable, and it incorporates fundamental assumptions about the spatial context of competition for patchy resources in blowflies, and may be applied to the optimization of mass rearing techniques and to the maintenance of insect colonies under experimental conditions.1254192153754

Learning Ensembles of Neural Networks by Means of a Bayesian Artificial Immune System

In this paper, we apply an immune-inspired approach to design ensembles of heterogeneous neural networks for classification problems. Our proposal, called Bayesian artificial immune system, is an estimation of distribution algorithm that replaces the traditional mutation and cloning operators with a probabilistic model, more specifically a Bayesian network, representing the joint distribution of promising solutions. Among the additional attributes provided by the Bayesian framework inserted into an immune-inspired search algorithm are the automatic control of the population size along the search and the inherent ability to promote and preserve diversity among the candidate solutions. Both are attributes generally absent from alternative estimation of distribution algorithms, and both were shown to be useful attributes when implementing the generation and selection of components of the ensemble, thus leading to high-performance classifiers. Several aspects of the design are illustrated in practical applications, including a comparative analysis with other attempts to synthesize ensembles.22230431

Hybrid neural networks: An evolutionary approach with local search

Considering computational algorithms available in the literature, associated with supervised learning in feedforward neural networks, a wide range of distinct approaches can be identified. While the adjustment of the connection weights represents an omnipresent stage, the algorithms differ on three basic aspects: the technique chosen to determine the dimension of the multilayer neural network, the procedure adopted to specify the activation functions, and the kind of composition used to produce the output. Advanced learning algorithms should be developed to simultaneously treat all these aspects during learning, and an evolutionary learning algorithm with local search is proposed here. The essence of this approach is a synergy between genetic algorithms and conjugate gradient optimization, operating on a hybrid neural network architecture. As a consequence, the final neural network is automatically generated, and is characterized to be dedicated and computationally parsimonious.91577

Hybridizing mixtures of experts with support vector machines: Investigation into nonlinear dynamic systems identification

Mixture of experts (ME) models comprise a family of modular neural network architectures aiming at distilling complex problems into simple subtasks. This is done by deploying a separate gating module for softly dividing the input space into overlapping regions to be each assigned to one or more expert networks. Conversely, support vector machines (SVMs) refer to kernel-based methods, neural-network-alike models that constitute an approximate implementation of the structural risk minimization principle. Such learning machines follow the simple, but powerful idea of nonlinearly mapping input data into high-dimensional feature spaces wherein a linear decision surface discriminating different regions is properly designed. In this work, we formally characterize and empirically evaluate a novel approach, named as Mixture of Support Vector Machine Experts (MSVME), whose main purpose is to combine the complementary properties of both SVM and ME models. In the formal characterization, an algorithm based on a maximum likelihood criterion is considered for the MSVME training, and we demonstrate that it is possible to train each expert based on an SVM perspective. Regarding the empirical evaluation, simulation results involving nonlinear dynamic system identification problems are reported, contrasting the performance shown by the MSVME approach with that exhibited by conventional SVM and ME models. (C) 2007 Elsevier Inc. All rights reserved.177102049207

Learning and optimization using the clonal selection principle

The clonal selection principle is used to explain the basic features of an adaptive immune response to an antigenic stimulus. It establishes the idea that only those cells that recognize the antigens (Ag's) are selected to proliferate. The selected cells are subject to an affinity maturation process, which improves their affinity to the selective Ag's. This paper proposes a computational implementation of the clonal selection principle that explicitly takes into account the affinity maturation of the immune response. The general algorithm, named CLONALG, is derived primarily to perform machine-learning and pattern-recognition tasks and then it is adapted to solve optimization problems, emphasizing multimodal and combinatorial optimization. Two versions of the algorithm are derived, their computational cost per iteration is presented, and a sensitivity analysis in relation to the user-defined parameters is given. CLONALG is also contrasted with evolutionary algorithms. Several benchmark problems are considered to evaluate the performance of CLONALG and it is also compared to a niching method for multimodal function optimization.6323925