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The effects of crime are diverse and complex, ranging from psychological and physical traumas faced by crime victims, to negative impacts on the economy of a whole nation. In this paper, an agent-based crime simulation framework to analyze crime and its causes is proposed and implemented. The agent-based simulation framework models and simulates both 1) crime events as a consequence of a set of interrelated social and individual-level crime factors, and 2) crime opportunities, i.e., combinations of circumstances that enable a person to commit a crime. The selection of crime factors and design of agent models are supported by, and based on, existing criminological literature. In addition, the simulation results are validated and compared with macro-level crime patterns reported by various criminological research efforts.

In recent years, Evolutionary Algorithms EAs have been remarkably useful to improve the robustness of Artificial Neural Networks ANNs. This study introduces an experimental analysis using an EAs aimed to evolve ANNs architectures the FS-EPNet algorithm to understand how neural networks are evolved with a steady-state algorithm and compare the Single-step-ahead SSP and Multiple-step-ahead MSP methods for prediction tasks over two test sets. It was decided to test an inside-set during evolution and an outside-set after the whole evolutionary process has been completed to validate the generalization performance with the same method SSP or MSP. Thus, the networks may not be correctly evaluated misleading fitness if the single SSP is used during evolution inside-set and then the MSP at the end of it outside-set. The results show that the same prediction method should be used in both evaluation sets providing smaller errors on average.

Induction motors are the most common engine used worldwide. When they are summited to extensive working journals, e.g. in industry, faults may appear, generating a performance reduction on them. Several works have been focused on detecting early mechanical and electrical faults before damage appears in the motor. However, the main drawback of them is the complexity on the motor’s signal mathematical processing. In this paper, a new methodology is proposed for detecting misalignment faults in induction motors. Through signal vibration and orbital analysis, misalignment faults are studied, generating characteristically patterns that are used for fault identification. Artificial Neural Networks are evolved with an evolutionary algorithm for misalignment pattern recognition, using two databases (training and recovering respectively). The results obtained, indicate a good performance of Artificial Neural Networks with low confusion rates, using experimental patterns obtained from real situations where motors present a certain level of misalignment.

Chaotic time series have been successfully predicted with the EPNet algorithm through the evolution of artificial neural networks. However, the input feature selection problem has either not been fully explored before or has not been compared against other algorithms in the literature. This paper presents four algorithms derived from the classical EPNet algorithm to evolve the input feature selection in three different chaotic series: Logistic, Lorenz and Mackey-Glass. Additionally, some flaws in the prediction field that may be considered in future works are discussed. A comparison against previous work demonstrates that in most cases the specialization of the EPNet algorithm allows better solutions with a smaller number of generations.

Modularity is known to have benefits for neural systems and their evolution, and this paper aims to improve the evolutionary neural network algorithm EPNet to take advantage of those benefits. Neural networks exist with varying degrees of modularity ranging from pure modular networks characterized by disjoint partitions of hidden nodes with no communication between modules, to pure homogeneous networks with significant connections throughout. In between are apparently homogeneous networks that can be seen to have some degree of modularity if the hidden nodes are reorganized appropriately. In this paper, a modularity measure is presented and extended that can be applied to any neuron at any level in the network to provide a fine analysis of node partitioning. It also allows the rearrangement of nodes to create modules in homogeneous networks, and that is used to improve the EPNet algorithm to evolve modular neural networks. Experimental results on a simple classification task confirm that the new modular EPNet algorithm does indeed lead to more modular networks than the classical EPNet algorithm, without compromising the performance on the given task.

This work provides an analysis of using the evolutionary algorithm EPNet to create ensembles of artificial neural networks to solve a range of forecasting tasks. Several previous studies have tested the EPNet algorithm in the classification field, taking the best individuals to solve the problem and creating ensembles to improve the performance. But no studies have analyzed the behavior of the algorithm in detail for time series forecasting, nor used ensembles to try to improve the predictions. Thus, the aim of this work is to compare the ensemble approach, using two linear combination methods to calculate the output, against the best individual found. Since there are several parameters to adjust, experiments are set up to optimize them and improve the performance of the algorithm. The algorithm is tested on 21 time series of different behaviors. The experimental results show that, for time series forecasting, it is possible to improve the performance by using the ensemble method rather than using the best individual. This demonstrates that the information contained in the EPNet population is better than the information carried by any one individual.

In computer sciences, matrices are widely used for representing different kinds of information. Measuring similarity among square matrices is an interesting open problem in computer sciences. Furthermore, eigenvalues and eigenvectors are a powerful way for representing and characterizing square matrices. In this paper we introduce a new similarity measure among two square data matrices of the same class; the idea is based on evaluating the effect of conjugate the eigenvalues and eigenvectors of one matrix with the other matrix, and vice versa. Some experimental results are showed in order to analyze and exemplify the Eigenconjugation as an approach for the problem of similarity of matrices.

In computer sciences, matrices are widely used for representing different kinds of information. Furthermore, measuring similarity among matrices is an interesting open problem. In this paper we introduce a new similarity measure among two data matrices of the same class; the idea is based on evaluating the effect of conjugating the singular values and singular vectors of one matrix with the other matrix, and vice versa. A brief analysis of Singular Value Conjugation (SV-Conjugation) is also included in order to extend the understanding of the concept. Finally, some experimental results are presented with the aim of exemplify the usefulness of SV-Conjugation as an approach for the problem of computing similarity among data matrices and also among images.

We introduce a method for computing similarity between two square matrices based on the information given by their eigenvalues and eigenvectors. The idea is to evaluate the effect of the conjugation of the original matrices and the eigenvectors and eigenvalues of each other. Then, we exemplify its utility for computing similarity between square images from a classified bank of pictures. The performance of the method is evaluated with diverse experiments.

Similarity search in time series databases has been the focus of a lot research. It is non-trivial problem due their peculiar structure, high dimensionality (C. Faloutsos et. al, 1994), noise, and high feature correlation; it is also the heart of many data mining applications, like clustering, classification, rule discovery and query by content. In general, similarity has been measured with the Euclidean metric, but the dimensionality curse problem remains (K. Chan and A. Wai-chee, 1999). Basically, there are four major techniques that have been proposed in order to reduce the dimensionality of the data (E. Keogh et. al, 2000), singular value decomposition (SVD), the discrete Fourier transform (DFT), the discrete wavelets transform (DWT), and the piecewise aggregate approximation (PAA)(E. Keogh and M. Pazzami, 2000). In this work, we use the dynamic time warping (DTW) algorithm and PAA dimensionality reduction technique as a new approach to find the similarity between seismological signals.