Search results for "surrogate"

A Surrogate-assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-objective Optimization

2018

We propose a surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive optimization problems with more than three objectives. The proposed algorithm is based on a recently developed evolutionary algorithm for many-objective optimization that relies on a set of adaptive reference vectors for selection. The proposed surrogateassisted evolutionary algorithm uses Kriging to approximate each objective function to reduce the computational cost. In managing the Kriging models, the algorithm focuses on the balance of diversity and convergence by making use of the uncertainty information in the approximated objective values given by the Kriging models, the distr…

On Dealing with Uncertainties from Kriging Models in Offline Data-Driven Evolutionary Multiobjective Optimization

2019

Many works on surrogate-assisted evolutionary multiobjective optimization have been devoted to problems where function evaluations are time-consuming (e.g., based on simulations). In many real-life optimization problems, mathematical or simulation models are not always available and, instead, we only have data from experiments, measurements or sensors. In such cases, optimization is to be performed on surrogate models built on the data available. The main challenge there is to fit an accurate surrogate model and to obtain meaningful solutions. We apply Kriging as a surrogate model and utilize corresponding uncertainty information in different ways during the optimization process. We discuss…

Handling expensive multiobjective optimization problems with evolutionary algorithms

2017

Multiobjective optimization problems (MOPs) with a large number of conflicting objectives are often encountered in industry. Moreover, these problem typically involve expensive evaluations (e.g. time consuming simulations or costly experiments), which pose an extra challenge in solving them. In this thesis, we first present a survey of different methods proposed in the literature to handle MOPs with expensive evaluations. We observed that most of the existing methods cannot be easily applied to problems with more than three objectives. Therefore, we propose a Kriging-assisted reference vector guided evolutionary algorithm (K-RVEA) for problems with at least three expensive objectives. The alg…

Approximation through interpolation in nonconvex multiobjective optimization

2011

Probabilistic Selection Approaches in Decomposition-based Evolutionary Algorithms for Offline Data-Driven Multiobjective Optimization

2022

In offline data-driven multiobjective optimization, no new data is available during the optimization process. Approximation models, also known as surrogates, are built using the provided offline data. A multiobjective evolutionary algorithm can be utilized to find solutions by using these surrogates. The accuracy of the approximated solutions depends on the surrogates and approximations typically involve uncertainties. In this paper, we propose probabilistic selection approaches that utilize the uncertainty information of the Kriging models (as surrogates) to improve the solution process in offline data-driven multiobjective optimization. These approaches are designed for decomposition-base…

Approximation method for computationally expensive nonconvex multiobjective optimization problems

2012

How does the journal impact factor affect the CV of PhD students?

2014

In his editorial “Dear DORA”, Howy Jacobs commented on the recent San Francisco Declaration of Research Assessment (DORA) to address the misuse of the journal impact factor (IF) and discussed alternatives. DORA stipulates that the IF must not be used as a surrogate measure of the quality of individual research articles, or to assess an individual scientist's contributions in hiring, promotion and funding decisions. DORA and many other commentators, such as Howy Jacobs, therefore advocate the use of additional, alternative metrics and other measures to make a more fair and realistic judgment about the quality of a scientist's or …

Proces selekcji kandydatów partii politycznych w wyborach Prezydenta RP – motywacje, dylematy, strategie

2022

Wybory prezydenckie pomimo dużego stopnia spersonalizowania, stanowią ważną przestrzeń rywalizacji międzypartyjnej. Doświadczenia polskich elekcji prezydenckich pokazują, że są one zdominowane przez kandydatów partyjnych. Podstawowym celem niniejszego artykułu, jest ukazanie procesu selekcji kandydatów partii politycznych na urząd Prezydenta RP, jako jednego z kontekstów wyborów prezydenckich w Polsce. Istotnym punktem rozważań jest odniesienie wskazanego procesu do aktywności przywódców partyjnych w tym zakresie, a dokładnie zauważalnej tendencji do rezygnacji z kandydowania. Trend ten wiąże się z wykorzystywaniem przez partie polityczne tzw. surogatów, czyli kandydatów zastępczych. Porusz…

Zofenopril is a cost-effective treatment for patients with left ventricular systolic dysfunction following acute myocardial infarction: a pharmacoeco…

2013

Objective: The Survival of Myocardial Infarction Long-term Evaluation 4 Study (SMILE-4) showed the superiority of Zofenopril (Z) associated with Acetylsalicylic Acid (ASA) as respect to Ramipril (R) plus ASA in reducing the occurrence of major cardiovascular events, in patients with left ventricular dysfunction (LVD) following Acute Myocardial Infarction (AMI). The objective of this retrospective analysis was the evaluation of cost-effectiveness of Z compared to R. Methods: 771 patients with LVD and AMI were randomized, double-blind to Z 60 mg/day (n=389) or R 10 mg/day (n=382) plus ASA 100 mg/day and followed-up for 1 year. The primary study end-point was 1-year combined occurrence of deat…

Rare Cancers Europe (RCE) methodological recommendations for clinical studies in rare cancers: A European consensus position paper

2015

While they account for one-fifth of new cancer cases, rare cancers are difficult to study. A higher than average degree of uncertainty should be accommodated for clinical as well as for population-based decision making. Rules of rational decision making in conditions of uncertainty should be rigorously followed and would need widely informative clinical trials. In principle, any piece of new evidence would need to be exploited in rare cancers. Methodologies to explicitly weigh and combine all the available evidence should be refined, and the Bayesian logic can be instrumental to this end. Likewise, Bayesian-design trials may help optimize the low number of patients liable to be enrolled in …