Study of the performance of a resolution criterion to characterise complex chromatograms with unknowns or without standards

2017 ◽  
Vol 9 (29) ◽  
pp. 4293-4303 ◽  
Author(s):  
J. A. Navarro-Huerta ◽  
T. Alvarez-Segura ◽  
J. R. Torres-Lapasió ◽  
M. C. García-Alvarez-Coque
Keyword(s):  
Ideal Criterion ◽  
Pareto Fronts ◽  
Peak Purity

Validation of peak prominence (which does not need standards) versus peak purity (an ideal criterion using standards) based on the agreement of Pareto fronts.

scholarly journals Usage of the Pareto Fronts as a Tool to Select Data in the Forecasting Process—A Short-Term Electric Energy Demand Forecasting Case

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3204
Author(s):  
Michał Sabat ◽  
Dariusz Baczyński
Keyword(s):  
Energy Demand ◽  
Demand Forecasting ◽  
Electric Energy ◽  
Forecasting Model ◽  
Short Term ◽  
Complex Evaluation ◽  
Proposed Model ◽  
Energy Demand Forecasting ◽  
Pareto Fronts ◽  
Naive Approach

Transmission, distribution, and micro-grid system operators are struggling with the increasing number of renewables and the changing nature of energy demand. This necessitates the use of prognostic methods based on ever shorter time series. This study depicted an attempt to develop an appropriate method by introducing a novel forecasting model based on the idea to use the Pareto fronts as a tool to select data in the forecasting process. The proposed model was implemented to forecast short-term electric energy demand in Poland using historical hourly demand values from Polish TSO. The study rather intended on implementing the range of different approaches—scenarios of Pareto fronts usage than on a complex evaluation of the obtained results. However, performance of proposed models was compared with a few benchmark forecasting models, including naïve approach, SARIMAX, kNN, and regression. For two scenarios, it has outperformed all other models by minimum 7.7%.

scholarly journals Development of Multiple Behaviors in Evolving Robots

Robotics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 1
Author(s):  
Victor Massagué Respall ◽  
Stefano Nolfi
Keyword(s):  
Evolutionary Algorithms ◽  
State Of The Art ◽  
Evolutionary Robotics ◽  
The Other ◽  
Evolutionary Strategies ◽  
Multiple Behaviors ◽  
Pareto Fronts ◽  
Expected Fitness

We investigate whether standard evolutionary robotics methods can be extended to support the evolution of multiple behaviors by forcing the retention of variations that are adaptive with respect to all required behaviors. This is realized by selecting the individuals located in the first Pareto fronts of the multidimensional fitness space in the case of a standard evolutionary algorithms and by computing and using multiple gradients of the expected fitness in the case of a modern evolutionary strategies that move the population in the direction of the gradient of the fitness. The results collected on two extended versions of state-of-the-art benchmarking problems indicate that the latter method permits to evolve robots capable of producing the required multiple behaviors in the majority of the replications and produces significantly better results than all the other methods considered.

Using single-cell cytometry to illustrate integrated multi-perspective evaluation of clustering algorithms using Pareto fronts

2021 ◽  
Author(s):  
Givanna H Putri ◽  
Irena Koprinska ◽  
Thomas M Ashhurst ◽  
Nicholas J C King ◽  
Mark N Read
Keyword(s):  
Single Cell ◽  
Performance Metrics ◽  
Clustering Algorithms ◽  
Latin Hypercube Sampling ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Evaluation Protocol ◽  
Benchmark Datasets ◽  
Pareto Fronts ◽  
Parameter Values

Abstract Motivation Many ‘automated gating’ algorithms now exist to cluster cytometry and single-cell sequencing data into discrete populations. Comparative algorithm evaluations on benchmark datasets rely either on a single performance metric, or a few metrics considered independently of one another. However, single metrics emphasize different aspects of clustering performance and do not rank clustering solutions in the same order. This underlies the lack of consensus between comparative studies regarding optimal clustering algorithms and undermines the translatability of results onto other non-benchmark datasets. Results We propose the Pareto fronts framework as an integrative evaluation protocol, wherein individual metrics are instead leveraged as complementary perspectives. Judged superior are algorithms that provide the best trade-off between the multiple metrics considered simultaneously. This yields a more comprehensive and complete view of clustering performance. Moreover, by broadly and systematically sampling algorithm parameter values using the Latin Hypercube sampling method, our evaluation protocol minimizes (un)fortunate parameter value selections as confounding factors. Furthermore, it reveals how meticulously each algorithm must be tuned in order to obtain good results, vital knowledge for users with novel data. We exemplify the protocol by conducting a comparative study between three clustering algorithms (ChronoClust, FlowSOM and Phenograph) using four common performance metrics applied across four cytometry benchmark datasets. To our knowledge, this is the first time Pareto fronts have been used to evaluate the performance of clustering algorithms in any application domain. Availability and implementation Implementation of our Pareto front methodology and all scripts and datasets to reproduce this article are available at https://github.com/ghar1821/ParetoBench. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Localized probability of improvement for kriging based multi-objective optimization

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 954-958
Author(s):  
Yinjiang Li ◽  
Song Xiao ◽  
Paolo Di Barba ◽  
Mihai Rotaru ◽  
Jan K. Sykulski
Keyword(s):  
Uniform Distribution ◽  
Nearest Neighbor ◽  
Higher Dimensions ◽  
Multi Objective Optimization ◽  
New Approach ◽  
Multi Objective ◽  
Local Probability ◽  
Pareto Fronts ◽  
Probability Of Improvement ◽  
Infill Sampling

AbstractThe paper introduces a new approach to kriging based multi-objective optimization by utilizing a local probability of improvement as the infill sampling criterion and the nearest neighbor check to ensure diversification and uniform distribution of Pareto fronts. The proposed method is computationally fast and linearly scalable to higher dimensions.

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