A. de Carvalho

A Survey of Evolutionary Algorithms for Clustering

This paper presents a survey of evolutionary algorithms designed for clustering tasks. It tries to reflect the profile of this area by focusing more on those subjects that have been given more importance in the literature. In this context, most of the paper is devoted to partitional algorithms that look for hard clusterings of data, though overlapping (i.e., soft and fuzzy) approaches are also covered in the paper. The paper is original in what concerns two main aspects. First, it provides an up-to-date overview that is fully devoted to evolutionary algorithms for clustering, is not limited to any particular kind of evolutionary approach, and comprises advanced topics like multiobjective and ensemble-based evolutionary clustering. Second, it provides a taxonomy that highlights some very important aspects in the context of evolutionary data clustering, namely, fixed or variable number of clusters, cluster-oriented or nonoriented operators, context-sensitive or context-insensitive operators, guided or unguided operators, binary, integer, or real encodings, centroid-based, medoid-based, label-based, tree-based, or graph-based representations, among others. A number of references are provided that describe applications of evolutionary algorithms for clustering in different domains, such as image processing, computer security, and bioinformatics. The paper ends by addressing some important issues and open questions that can be subject of future research.

A Survey of Evolutionary Algorithms for Decision-Tree Induction

This paper presents a survey of evolutionary algorithms that are designed for decision-tree induction. In this context, most of the paper focuses on approaches that evolve decision trees as an alternate heuristics to the traditional top-down divide-and-conquer approach. Additionally, we present some alternative methods that make use of evolutionary algorithms to improve particular components of decision-tree classifiers. The papers original contributions are the following. First, it provides an up-to-date overview that is fully focused on evolutionary algorithms and decision trees and does not concentrate on any specific evolutionary approach. Second, it provides a taxonomy, which addresses works that evolve decision trees and works that design decision-tree components by the use of evolutionary algorithms. Finally, a number of references are provided that describe applications of evolutionary algorithms for decision-tree induction in different domains. At the end of this paper, we address some important issues and open questions that can be the subject of future research.

Neural networks applied in intrusion detection systems

Information is one of the most valuable possessions today. As the Internet expands both in number of hosts connected and number of services provided, security has become a key issue for the technology developers. This work presents a prototype of an intrusion detection system for TCP/IP networks. The system works by capturing packets and using a neural network to identify an intrusive behavior within the analyzed data stream. The identification is based on previous well know intrusion profiles. The system is adaptive, since new profiles can be added to the data base and the neural network retrained to consider them. We present the proposed model, the results achieved and the analysis of an implemented prototype.

Combining intelligent techniques for sensor fusion

Mobile robots rely on sensor data to build a representation of their environment. However, sensors usually provide incomplete, inconsistent or inaccurate information. Sensor fusion has been successfully employed to enhance the accuracy of sensor measures. This work proposes and investigates the use of Artificial Intelligence techniques for sensor fusion. Its main goal is to improve the accuracy and reliability of the distance measure between a robot and an object in its work environment, based on measures obtained from different sensors. Several Machine Learning algorithms are investigated to fuse the sensors data. The best model generated by each algorithm is called estimator. It is shown that the employment of estimators based on Artificial Intelligence can improve significantly the performance achieved by each sensor alone. The Machine Learning algorithms employed have different characteristics, causing the estimators to have different behaviors in different situations. Aiming to achieve an even more accurate and reliable behavior, the estimators are combined in committees. The results obtained suggest that this combination can further improve the reliability and accuracy of the distances measured by the individual sensors and estimators used for sensor fusion.

Support vector machines applied to white blood cell recognition

A clinical decision support system known as Leuko has been developed for leukemia diagnosis using a naive Bayes classifier. The system is able to recognize six types of white blood cells (WBC), including a malignancy. This paper investigates the use of support vector machines (SVMs) classifiers to recognize WBC for future leukemia diagnosis. Since SVMs are originally designed for the solution of two class problems, several strategies for their extension to this multiclass task are investigated and compared. The experimental results evidence the potential of SVMs to leukemia diagnosis and indicate that a hierarchical tree-based multiclass strategy can be better suited to a future update of the Leuko system.

Optimal energy restoration in radial distribution systems using a genetic approach and graph chain representation

Abstract Several approaches have been developed to restore energy in distribution systems after the interruption of services for part of the system. In order to achieve the energy restoration plan, these approaches usually perform the restoration for circuits with specific features. Additionally, they are not able to deal with large outage zones. This paper develops a method using Genetic algorithms for energy restoration in radial distribution systems. This algorithm overcomes the combinatorial characteristic and deals with non-linear and non-continuous objective functions, inherent to this kind of problem. A new representation for distribution systems is also proposed aiming to avoid the generation of unfeasible configurations (with outage zones and/or closed loops). The generation of only feasible configurations saves RAM memory and processing time. The efficiency of the proposed approach is shown using a fairly complex distribution system.

Evolutionary optimization of RBF networks

One of the main obstacles to the widespread use of artificial neural networks is the difficulty of adequately defining values for their free parameters. This article discusses how Radial Basis Function (RBF) networks can have their parameters defined by genetic algorithms. For such, it presents an overall view of the problems involved and the different approaches used to genetically optimize RBF networks. A new strategy to optimize RBF networks using genetic algorithms is proposed, which includes new representation, crossover operator and the use of a multiobjective optimization criterion. Experiments using a benchmark problem are performed and the results achieved using this model are compared to those achieved by other approaches.

Evolutionary Fuzzy Clustering: An Overview and Efficiency Issues

Clustering algorithms have been successfully applied to several data analysis problems in a wide range of domains, such as image processing, bioinformatics, crude oil analysis, market segmentation, document categorization, and web mining. The need for organizing data into categories of similar objects has made the task of clustering very important to these domains. In this context, there has been an increasingly interest in the study of evolutionary algorithms for clustering, especially those algorithms capable of finding blurred clusters that are not clearly separated from each other. In particular, a number of evolutionary algorithms for fuzzy clustering have been addressed in the literature. This chapter has two main contributions. First, it presents an overview of evolutionary algorithms designed for fuzzy clustering. Second, it describes a fuzzy version of an evolutionary algorithm for clustering, which has shown to be more computationally efficient than systematic (i.e., repetitive) approaches when the number of clusters in a data set is unknown. Illustrative experiments showing the influence of local optimization on the efficiency of the evolutionary search are also presented. These experiments reveal interesting aspects of the effect of an important parameter found in many evolutionary algorithms for clustering, namely, the number of iterations of a given local search procedure to be performed at each generation.

A Strategy for the Selection of Solutions of the Pareto Front Approximation in Multi-objective Clustering Approaches

One of the advantages of Pareto-based multi-objective genetic algorithms for clustering, when compared to classical clustering algorithms, is that, instead of a single solution (partition), they give as an output a set of solutions (approximation of the Pareto front or Pareto front, for short). However, such a set could be very large (e.g., hundreds of partitions) and, consequently, difficult to be analyzed manually. We present a selection strategy, based on the corrected Rand index, that aims at recommending, as final solution for Pareto-based multi-objective genetic algorithm approaches, a subset of partitions from the Pareto front. This subset should be much smaller than the the latter and, at the same time, keep the quality and the diversity of the partitions. In order to test our strategy, we develop a study of case in which we apply the strategy to the sets of solutions obtained with the multi-objective clustering ensemble algorithm (MOCLE) in the context of several data sets.

Bio-inspired Optimization Techniques for SVM Parameter Tuning

Machine learning techniques have been successfully applied to a large number of classification problems. Among these techniques, support vector machines (SVMs) are well know for the good classification accuracies reported in several studies. However, like many machine learning techniques, the classification performance obtained by SVMs is influenced by the choice of proper values for their free parameters. In this paper, we investigate what is the influence of different optimization techniques inspired by biology when they are used to optimize the free parameters of SVMs. This comparative study also included the default values suggested in the literature for the free parameters and a grid algorithm used for parameter tuning. The results obtained suggest that, although SVMs work well with the default values, they can benefit from the use of an optimization technique for parameter tuning.