Suchergebnisse

ADVANCES IN EVOLUTIONARY ALGORITHMS RESEARCH -- ADVANCES IN EVOLUTIONARY ALGORITHMS RESEARCH -- Library of Congress Cataloging-in-Publication Data -- CONTENTS -- PREFACE -- Chapter 1: EVOLUTIONARY MULTIMODAL OPTIMIZATION: A SHORT SURVEY -- Abstract -- 1. Introduction and Background -- 2. Problem Definition -- 3. Methodology -- 4. Benchmarking -- 5. Application -- 6. Discussion -- References -- Chapter 2: TOWARDS RESILIENT MULTIMEMETIC SYSTEMS ON UNSTABLE NETWORKS WITH COMPLEX TOPOLOGY -- Abstract -- 1. Introduction -- 2. Modeling a Dynamic Computational Environment

This paper describes the influence of setting control parameters of a differential evolutionary algorithm (DE) and the influence of adapting these parameters on the simulation of electric circuits and their components. Various DE algorithm strategies are investigated, and also the influence of adapting the controlling parameters (Cr, F) during simulation and the effect of sample size. Optimizing an equivalent circuit diagram is chosen as a test task. Several strategies and settings of a DE algorithm are evaluated according to their convergence to the right solution.

In this article we present a strategy based on an evolutionary algorithm to calculate the real vehicle ows in cities according to data from sensors placed in the streets. We have worked with a map imported from OpenStreetMap into the SUMO traffic simulator so that the resulting scenarios can be used to perform different optimizations with the confidence of being able to work with a traffic distribution close to reality. We have compared the results of our algorithm to other competitors and achieved results that replicate the real traffic distribution with a precision higher than 90%. ; Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This research has been partially funded by project number 8.06/5.47.4142 in collaboration with the VSB-Technical University of Ostrava and Universidad de Málaga UMA/FEDER FC14-TIC36, programa de fortalecimiento de las capacidades de I+D+i en las universidades 2014-2015, de la Consejería de Economía, Innovación, Ciencia y Empleo, cofinanciado por el fondo europeo de desarrollo regional (FEDER). Also, partially funded by the Spanish MINECO project TIN2014-57341-R (http://moveon.lcc.uma.es). The authors would like to thank the FEDER of European Union for financial support via project Movilidad Inteligente: Wi-Fi, Rutas y Contaminación (maxCT) of the "Programa Operativo FEDER de Andalucía 2014-2020. We also thank all Agency of Public Works of Andalusia Regional Government staff and researchers for their dedication and professionalism. Daniel H. Stolfi is supported by a FPU grant (FPU13/00954) from the Spanish Ministry of Education, Culture and Sports.

This paper presents a novel method for visualizing the dynamics of evolutionary algorithms in the form of complex networks. We discuss the analogy between individuals in populations in an arbitrary evolutionary algorithm and vertices of a complex network, as well as between edges in a complex network and communication between individuals in a population. The possibility of visualizing the dynamics of a complex network using the coupled map lattices method and control by means of chaos control techniques are also discussed. ; Grant Agency of the Czech Republic GACRGrant Agency of the Czech Republic [102/09/1680]; European Union Structural FundsEuropean Commission [CZ.1.05/1.1.00/02.0070]; state budget of the Czech Republic; CEBIA-Tech [CZ.1.05/2.1.00/03.0089]

Wireless sensor networks are highly useful for many location-sensitive applications including environmental monitoring, military applications, disaster management, etc. Localization in wireless sensor networks concerns about the precise estimation of node positions given a relatively small portion as anchor nodes with their absolute positions predetermined. Intrinsically, localization is an unconstrained optimization problem based on various distance/path measures. Most of the existing work focus on increasing the accuracy in position estimation typically by using different heuristic-based or mathematical techniques. On the other hand, there were many complex optimization problems successfully tackled by the nature inspired search algorithms including the ant-based or genetic algorithms. In this paper, we propose to adapt an evolutionary approach, namely a microgenetic algorithm, and integrate as a post-optimizer into some existing localization techniques such as the Ad-hoc Positioning System (APS) to further improve their position estimation. Clearly, our proposed MGA is so adaptable that it can easily be integrated into other localization methods. More importantly, the remarkable improvements obtained by the prototype of our proposed evolutionary optimizer on certain anisotropic topologies of our simulation tests prompt for further investigation. © 2006 IEEE. ; published_or_final_version

The amount of public statistical information available is growing and more accurate protection methods are needed in order to achieve data confidentiality. The Post-Randomization Method (PRAM) protection method was introduced in 1997 as a very powerful method for categorical microdata, but it is still not widely used. This method has a Markov matrix as a parameter. The main problem of the application of this method is that it is difficult to find a good Markov matrix that performs changes in the microdata file producing low loss of valuable information and low risk of disclosure of sensitive data. In this paper we present a methodology that helps us to find a matrix to perform better protections. This is achieved by using an evolutionary algorithm with integrated Information Loss and Disclosure Risk measures. Experiments using three different datasets are also presented in order to empirically evaluate the application of this technique. © 2014 Elsevier Inc. All rights reserved. ; This work has been done under the PhD in Computer Science program of the Universitat Autònoma de Barcelona (UAB). It is also partially supported by the Spanish MEC ARES-CONSOLIDER INGENIO 2010 CSD2007-00004, and COPRIVACY TIN2011-27076-C03-03. The research leading to these results has also received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under Grant Agreement Num. 262608. ; Peer Reviewed

In this study a general evolutionary algorithm is used to minimise the mass of a bumper beam according to FMVSS pendulum test specification (repair test). In contrast to most of the studies realised in the past, the focus is here on true shape optimisation with variation of the cross-sectional parameters. Special interest is spent in this paper on evaluating the ability of the algorithmic approach to obtain the global optimum. For this, a combined, i.e. sequential, global and local search strategy is employed. Finally, a sensitivity analysis of the optimum is performed again investigating the influence of the new type of shape optimisation parameters. In addition, the representation of different crash scenarios by the bumper legislation test is investigated. The results show (through a routine) that the identified optimum was with high probability the global minimum. The optimisation result is obtained even by repeated realisations and it is robust, i.e. the influence of small variations in noise variables (e.g. impactor shape and orientation) on the outputs is small. However if other crash scenarios are investigated represented by larger variations of impactor shape and orientation, the identified optimal design loses its quality and especially violates the deflection constraint. This indicates, that designing for a pre-defined repair test does not guarantee low repair costs in all relevant cases. Here, it is recommended to establish a hybrid approach for repair cost assessment based on partially virtual and partially physical testing. Hence, this paper concludes with few proposals for changes or addition in repair and legislation tests.