Level-Based Analysis of the Population-Based Incremental Learning Algorithm

The Population-Based Incremental Learning (PBIL) algorithm uses a convex combination of the current model and the empirical model to construct the next model, which is then sampled to generate offspring. The Univariate Marginal Distribution Algorithm (UMDA) is a special case of the PBIL, where the current model is ignored. Dang and Lehre (GECCO 2015) showed that UMDA can optimise LeadingOnes efficiently. The question still remained open if the PBIL performs equally well. Here, by applying the level-based theorem in addition to Dvoretzky--Kiefer--Wolfowitz inequality, we show that the PBIL optimises function LeadingOnes in expected time $\mathcal{O}(n\lambda \log \lambda + n^2)$ for a population size $\lambda = \Omega(\log n)$, which matches the bound of the UMDA. Finally, we show that the result carries over to BinVal, giving the fist runtime result for the PBIL on the BinVal problem.Comment: To appea

Rethinking Service Systems and Public Policy: A Transformative Refugee Service Experience Framework

The global refugee crisis is a complex humanitarian problem. Service researchers can assist in solving this crisis because refugees are immersed in complex human service systems. Drawing on marketing, sociology, transformative service, and consumer research literature, this study develops a Transformative Refugee Service Experience Framework to enable researchers, service actors, and public policy makers to navigate the challenges faced throughout a refugee’s service journey. The primary dimensions of this framework encompass the spectrum from hostile to hospitable refugee service systems and the resulting suffering or well-being in refugees’ experiences. The authors conceptualize this at three refugee service journey phases (entry, transition, and exit) and at three refugee service system levels (macro, meso, and micro) of analysis. The framework is supported by brief examples from a range of service-related refugee contexts as well as a Web Appendix with additional cases. Moreover, the authors derive a comprehensive research agenda from the framework, with detailed research questions for public policy and (service) marketing researchers. Managerial directions are provided to increase awareness of refugee service problems; stimulate productive interactions; and improve collaboration among public and nonprofit organizations, private service providers, and refugees. Finally, this work provides a vision for creating hospitable refugee service systems

How to Escape Local Optima in Black Box Optimisation: When Non-elitism Outperforms Elitism

Escaping local optima is one of the major obstacles to function optimisation. Using the metaphor of a fitness landscape, local optima correspond to hills separated by fitness valleys that have to be overcome. We define a class of fitness valleys of tunable difficulty by considering their length, representing the Hamming path between the two optima and their depth, the drop in fitness. For this function class we present a runtime comparison between stochastic search algorithms using different search strategies. The ((Formula presented.)) EA is a simple and well-studied evolutionary algorithm that has to jump across the valley to a point of higher fitness because it does not accept worsening moves (elitism). In contrast, the Metropolis algorithm and the Strong Selection Weak Mutation (SSWM) algorithm, a famous process in population genetics, are both able to cross the fitness valley by accepting worsening moves. We show that the runtime of the ((Formula presented.)) EA depends critically on the length of the valley while the runtimes of the non-elitist algorithms depend crucially on the depth of the valley. Moreover, we show that both SSWM and Metropolis can also efficiently optimise a rugged function consisting of consecutive valleys

Experimental identification of an uncertain computational dynamical model representing a family of structures

International audienceWe are interested in constructing an uncertain computational model representing a family of structures and in identifying this model using a small number of experimental measurements of the first eigenfrequencies. The prior probability model of uncertainties is constructed using the generalized probabilistic approach of uncertainties which allows both system-parameters uncertainties and model uncertainties to be taken into account. The parameters of the prior probability model of uncertainties are separately identified for each type of uncertainties, yielding an optimal prior probability model. The optimal prior stochastic computational model allows a robust analysis for the family of structures to be carried out

Construction and experimental identification of an uncertain model in computational dynamics using a generalized probabilistic approach of uncertainties

International audienceWe are interested in constructing an uncertain model of a nominal motor CSS of pressurized water reactor using a generalized probabilistic approach of uncertainties and in identifying this model using experimental measurements of the first eigenfrequencies. This generalized probabilistic approach of uncertainties allows both model-parameter uncertainties and model uncertainties to be taken into account and identified separately in the context of the experimental modal anaysis. Finally, the identified uncertain model allows statistics on quantities of interest to be estimated

Using model reduction and data expansion techniques to improve SDM

A method designed to predict the effects of distributed modifications of structures is proposed here. This method is an evolution of the classical formulations, and distinguishes measurements and coupling points. Based on a coarse model of the structure to be modified, the proposed methodology tackles two major difficulties: efficient predictions for distributed modifications and handling of the lack of measurement points on the coupling interface. In addition, displacements bases introduced to reconstruct unmeasured behaviour of the interface limit error propagation through the process. Moreover, two indicators are introduced to select the optimal prediction

Amélioration de méthodes de modification structurale par utilisation de techniques d'expansion et de réduction d'interface : applications

Une méthode proposant d'estimer de l'effet de modifications structurales distribuées est proposée ici. Une évolution des formulations classiques permettant de prendre en compte la non coïncidence des points de mesure et des points de couplage est présentée. La mise en œuvre de la méthodologie à un exemple académique et un cas d'application industriel permet de mettre en lumière les avantages de la méthodologie exposée