Multiobjective Algorithms Hybridization to Optimize Broadcasting Parameters in Mobile Ad-Hoc Networks

Proceeding of: 10th InternationalWork-Conference on Artificial Neural Networks, IWANN 2009 Salamanca, Spain, June 10-12, 2009The aim os this paper is to study the hybridization of two multi-objective algorithms in the context of a real problem, the MANETs problem. The algorithms studied are Particle Swarm Optimization (MOPSO) and a new multiobjective algorithm based in the combination of NSGA-II with Evolution Strategies (ESN). This work analyzes the improvement produced by hybridization over the Pareto’s fronts compared with the non-hybridized algorithms. The purpose of this work is to validate how hybridization of two evolutionary algorithms of different families may help to solve certain problems together in the context of MANETs problem. The hybridization used for this work consists on a sequential execution of the two algorithms and using the final population of the first algorithm as initial population of the second one.This article has been financed by the Spanish founded research MEC projects OPLINK::UC3M, Ref:TIN2005-08818- C04-02 and MSTAR::UC3M, Ref:TIN2008-06491-C04-03.Publicad

Population Sizing for the Redundant Trivial Voting Mapping

Mapping-Proble

Regression Error Characteristic Optimisation of Non-Linear Models.

Copyright © 2006 Springer-Verlag Berlin Heidelberg. The final publication is available at link.springer.comBook title: Multi-Objective Machine LearningIn this chapter recent research in the area of multi-objective optimisation of regression models is presented and combined. Evolutionary multi-objective optimisation techniques are described for training a population of regression models to optimise the recently defined Regression Error Characteristic Curves (REC). A method which meaningfully compares across regressors and against benchmark models (i.e. ‘random walk’ and maximum a posteriori approaches) for varying error rates. Through bootstrapping training data, degrees of confident out-performance are also highlighted

Multi-objective optimisation for receiver operating characteristic analysis

Copyright © 2006 Springer-Verlag Berlin Heidelberg. The final publication is available at link.springer.comBook title: Multi-Objective Machine LearningSummary Receiver operating characteristic (ROC) analysis is now a standard tool for the comparison of binary classifiers and the selection operating parameters when the costs of misclassification are unknown. This chapter outlines the use of evolutionary multi-objective optimisation techniques for ROC analysis, in both its traditional binary classification setting, and in the novel multi-class ROC situation. Methods for comparing classifier performance in the multi-class case, based on an analogue of the Gini coefficient, are described, which leads to a natural method of selecting the classifier operating point. Illustrations are given concerning synthetic data and an application to Short Term Conflict Alert

Using Co-solvability to Model and Exploit Synergetic Effects in Evolution

We introduce, analyze, and experimentally verify the concept of co-solvability, meant as the ability of a solution maintained by an evolutionary run to solve (correctly process) a pair of fitness cases (tests). The method based on this concept can be considered as a second-order implicit fitness sharing, where solutions compete for the rewards granted for solving pairs of tests, rather than single tests. We prove that co-solvability fitness function is by definition synergistic and imposes selection pressure which is qualitatively different from that induced by standard fitness function or implicit fitness sharing. The results of experimental verification on eight genetic programming tasks demonstrate that evolutionary runs driven by the proposed fitness function usually converge faster to global optima than other methods

Optimizing the DFCN Broadcast Protocol with a Parallel Cooperative Strategy of Multi-Objective Evolutionary Algorithms

Proceeding of: 5th International Conference, EMO 2009, Nantes, France, April 7-10, 2009This work presents the application of a parallel coopera- tive optimization approach to the broadcast operation in mobile ad-hoc networks (manets). The optimization of the broadcast operation im- plies satisfying several objectives simultaneously, so a multi-objective approach has been designed. The optimization lies on searching the best configurations of the dfcn broadcast protocol for a given manet sce- nario. The cooperation of a team of multi-objective evolutionary al- gorithms has been performed with a novel optimization model. Such model is a hybrid parallel algorithm that combines a parallel island- based scheme with a hyperheuristic approach. Results achieved by the algorithms in different stages of the search process are analyzed in order to grant more computational resources to the most suitable algorithms. The obtained results for a manets scenario, representing a mall, demon- strate the validity of the new proposed approach.This work has been supported by the ec (feder) and the Spanish Ministry of Education and Science inside the ‘Plan Nacional de i+d+i’ (tin2005-08818-c04) and (tin2008-06491-c04-02). The work of Gara Miranda has been developed under grant fpu-ap2004-2290.Publicad

Complementary roles for scavenger receptor A and CD36 of human monocyte-derived macrophages in adhesion to surfaces coated with oxidized low-density lipoproteins and in secretion of H2O2

Oxidized low-density lipoprotein (oxLDL) is considered one of the principal effectors of atherogenesis. To explore mechanisms by which oxLDL affects human mononuclear phagocytes, we incubated these cells in medium containing oxLDL, acetylated LDL (acLDL), or native LDL, or on surfaces coated with these native and modified lipoproteins. The presence of soluble oxLDL, acLDL, or native LDL in the medium did not stimulate H2O2 secretion by macrophages. In contrast, macrophages adherent to surfaces coated with oxLDL secreted three- to fourfold more H2O2 than macrophages adherent to surfaces coated with acLDL or native LDL. Freshly isolated blood monocytes secreted little H2O2 regardless of the substrate on which they were plated. H2O2 secretion was maximal in cells maintained for 4–6 d in culture before plating on oxLDL-coated surfaces. Fucoidan, a known ligand of class A macrophage scavenger receptors (MSR-A), significantly reduced macrophage adhesion to surfaces coated with oxLDL or acLDL. Monoclonal antibody SMO, which blocks oxLDL binding to CD36, did not inhibit adhesion of macrophages to oxLDL-coated surfaces but markedly reduced H2O2 secretion by these cells. These studies show that MSR-A is primarily responsible for adhesion of macrophages to oxLDL-coated surfaces, that CD36 signals H2O2 secretion by macrophages adherent to these surfaces, and that substrate-bound, but not soluble, oxLDL stimulates H2O2 secretion by macrophages

Determination of alphaS from Hadronic Event Shapes in e+e- Annihilation at 192 < sqrt(s) < 208 GeV

Results are presented from a study of the structure of high energy hadronic events recorded by the L3 detector at sqrt(s)>192 GeV. The distributions of several event shape variables are compared to resummed O(alphaS^2) QCD calculations. We determine the strong coupling constant at three average centre-of-mass energies: 194.4, 200.2 and 206.2 GeV. These measurements, combined with previous L3 measurements at lower energies, demonstrate the running of alphaS as expected in QCD and yield alphaS(mZ) = 0.1227 +- 0.0012 +- 0.0058, where the first uncertainty is experimental and the second is theoretical

Measurement of the B0-anti-B0-Oscillation Frequency with Inclusive Dilepton Events

The $B^0$-$\bar B^0$ oscillation frequency has been measured with a sample of 23 million \B\bar B pairs collected with the BABAR detector at the PEP-II asymmetric B Factory at SLAC. In this sample, we select events in which both B mesons decay semileptonically and use the charge of the leptons to identify the flavor of each B meson. A simultaneous fit to the decay time difference distributions for opposite- and same-sign dilepton events gives $\Delta m_d = 0.493 \pm 0.012{(stat)}\pm 0.009{(syst)}$ ps$^{-1}$.Comment: 7 pages, 1 figure, submitted to Physical Review Letter