Adaptation and application of multi-objective evolutionary algorithms for rule reduction and parameter tuning of fuzzy rule-based systems

Recently, multi-objective evolutionary algorithms have been applied to improve the difficult tradeoff between interpretability and accuracy of fuzzy rule-based systems. It is known that both requirements are usually contradictory, however, these kinds of algorithms can obtain a set of solutions with different trade-offs. This contribution analyzes different application alternatives in order to attain the desired accuracy/interpr-etability balance by maintaining the improved accuracy that a tuning of membership functions could give but trying to obtain more compact models. In this way, we propose the use of multi-objective evolutionary algorithms as a tool to get almost one improved solution with respect to a classic single objective approach (a solution that could dominate the one obtained by such algorithm in terms of the system error and number of rules). To do that, this work presents and analyzes the application of six different multi-objective evolutionary algorithms to obtain simpler and still accurate linguistic fuzzy models by performing rule selection and a tuning of the membership functions. The results on two different scenarios show that the use of expert knowledge in the algorithm design process significantly improves the search ability of these algorithms and that they are able to improve both objectives together, obtaining more accurate and at the same time simpler models with respect to the single objective based approach.

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Multiple parallel FPGA implementations of a Kolmogorov phase screen generator

Modelling the effects of wavefront distortions over a finite aperture is an essential component in the simulation of adaptive optics configurations, prediction of performance of laser designators and atmospheric imaging simulations like generation of infrared (IR) scenes in the presence of atmospheric turbulence. In all of these applications many thousands of phase screens need to be generated. The computation time required for a large iterations of algorithms that model this effect is important an issue and for this reason there have been many previous attempts to improve the computation speed such algorithms. In this paper, the computation performance of the best previous algorithm that models this phenomenon is substantially improved using high performance reconfigurable computing through acceleration of the key computationally intensive steps of the algorithm on a field programmable gate array (FPGA). Our best hardware implementation can provide a speedup of more than 60 times the original algorithm.

Indoor navigation of a non-holonomic mobile robot using a visual memory

When navigating in an unknown environment for the first time, a natural behavior consists on memorizing some key views along the performed path, in order to use these references as checkpoints for a future navigation mission. The navigation framework for wheeled mobile robots presented in this paper is based on this assumption. During a human-guided learning step, the robot performs paths which are sampled and stored as a set of ordered key images, acquired by an embedded camera. The set of these obtained visual paths is topologically organized and provides a visual memory of the environment. Given an image of one of the visual paths as a target, the robot navigation mission is defined as a concatenation of visual path subsets, called visual route. When running autonomously, the robot is controlled by a visual servoing law adapted to its nonholonomic constraint. Based on the regulation of successive homographies, this control guides the robot along the reference visual route without explicitly planning any trajectory. The proposed framework has been designed for the entire class of central catadioptric cameras (including conventional cameras). It has been validated onto two architectures. In the first one, algorithms have been implemented onto a dedicated hardware and the robot is equipped with a standard perspective camera. In the second one, they have been implemented on a standard PC and an omnidirectional camera is considered.

Inverse multi-objective robust evolutionary design

In this paper, we present an Inverse Multi-Objective Robust Evolutionary (IMORE) design methodology that handles the presence of uncertainty without making assumptions about the uncertainty structure. We model the clustering of uncertain events in families of nested sets using a multi-level optimization search. To reduce the high computational costs of the proposed methodology we proposed schemes for (1) adapting the step-size in estimating the uncertainty, and (2) trimming down the number of calls to the objective function in the nested search. Both offline and online adaptation strategies are considered in conjunction with the IMORE design algorithm. Design of Experiments (DOE) approaches further reduce the number of objective function calls in the online adaptive IMORE algorithm. Empirical studies conducted on a series of test functions having diverse complexities show that the proposed algorithms converge to a set of Pareto-optimal design solutions with non-dominated nominal and robustness performances efficiently.

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Scalable landmark recognition using EXTENT

We have proposed the EXTENT system for automated photograph annotation using image content and context analysis. A key component of EXTENT is a Landmark recognition system called LandMarker. In this paper, we present the architecture of LandMarker. The content of a query photograph is analyzed and compared against a database of sample landmark images, to recognize any landmarks it contains. An algorithm is presented for comparing a query image with a sample image. Context information may be used to assist landmark recognition. Also, we show how LandMarker deals with scalability to allow recognition of a large number of landmarks. We have implemented a prototype of the system, and present empirical results on a large dataset.

The 2007 IEEE CEC simulated car racing competition

This paper describes the simulated car racing competition that was arranged as part of the 2007 IEEE Congress on Evolutionary Computation. Both the game that was used as the domain for the competition, the controllers submitted as entries to the competition and its results are presented. With this paper, we hope to provide some insight into the efficacy of various computational intelligence methods on a well-defined game task, as well as an example of one way of running a competition. In the process, we provide a set of reference results for those who wish to use the simplerace game to benchmark their own algorithms. The paper is co-authored by the organizers and participants of the competition.

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A Fast Marching Method for the Area Based Affine Distance

In a previous paper, it was proved that the area based affine distance of a convex region in the plane satisfies a non-homogeneous Monge-Ampère differential equation. Based on this equation, in this paper we propose a fast marching method for the computation of this distance. The proposed algorithm has a lower computational complexity than the direct method and we have proved its convergence. And since the algorithm allows one to obtain a connection from any point of the region to the boundary by a path of decreasing distance, it offers a dynamic point of view for the area based affine distance.

Fast adaptive termination mode selection for H.264 scalable video coding

Although scalable video coding can achieve coding efficiencies comparable with single layer video coding, its computational complexity is higher due to its additional inter-layer prediction process. This paper presents a fast adaptive termination algorithm for mode selection to increase its computation speed while attempting to maintain its coding efficiency. The developed algorithm consists of the following three main steps which are applied not only to the enhancement layer but also to the base layer: a prediction step based on neighboring macroblocks, a first round check step, and a second round check step or refinement if failure occurs during the first round check. Comparison results with the existing algorithms are provided. The results obtained on various video sequences show that the introduced algorithm achieves about one-third reduction in the computation speed while generating more or less the same video quality.

Exploiting maximal redundancy to optimize SQL queries

Detecting and dealing with redundancy is an ubiquitous problem in query optimization, which manifests itself in many areas of research such as materialized views, multi-query optimization, and query-containment algorithms. In this paper, we focus on the issue of intra-query redundancy, redundancy present within a query. We present a method to detect the maximal redundancy present between a main (outer) query block and a subquery block. We then use the method for query optimization, introducing query plans and a new operator that take full advantage of the redundancy discovered. Our approach can deal with redundancy in a wider spectrum of queries than existing techniques. We show experimental evidence that our approach works under certain conditions, and compares favorably to existing optimization techniques when applicable.

A Framework for Evaluating Privacy Preserving Data Mining Algorithms<Superscript>*</Superscript>

Recently, a new class of data mining methods, known as privacy preserving data mining (PPDM) algorithms, has been developed by the research community working on security and knowledge discovery. The aim of these algorithms is the extraction of relevant knowledge from large amount of data, while protecting at the same time sensitive information. Several data mining techniques, incorporating privacy protection mechanisms, have been developed that allow one to hide sensitive itemsets or patterns, before the data mining process is executed. Privacy preserving classification methods, instead, prevent a miner from building a classifier which is able to predict sensitive data. Additionally, privacy preserving clustering techniques have been recently proposed, which distort sensitive numerical attributes, while preserving general features for clustering analysis. A crucial issue is to determine which ones among these privacy-preserving techniques better protect sensitive information. However, this is not the only criteria with respect to which these algorithms can be evaluated. It is also important to assess the quality of the data resulting from the modifications applied by each algorithm, as well as the performance of the algorithms. There is thus the need of identifying a comprehensive set of criteria with respect to which to assess the existing PPDM algorithms and determine which algorithm meets specific requirements. In this paper, we present a first evaluation framework for estimating and comparing different kinds of PPDM algorithms. Then, we apply our criteria to a specific set of algorithms and discuss the evaluation results we obtain. Finally, some considerations about future work and promising directions in the context of privacy preservation in data mining are discussed. ^*The work reported in this paper has been partially supported by the EU under the IST Project CODMINE and by the Sponsors of CERIAS. Editor:  Geoff Webb

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