Results 181 to 190 of about 2,735,316 (329)

Pareto front for chemotherapy scheludes

, 2012
In this paper we consider a multi-objective approach to chemother-apy optimization. We assume that the dynamics of the tumor are mod-eling for the stochastic Gompertz growth model with a linear cell-losseffect . We consider fuzzy constraints for the problem.
Barrea, Andres Alberto, Hernandez, Matias Ezequiel   +1 more
openaire   +1 more source

Life‐history stages and behavior influence demographic classification of moose captured on remote cameras

Wildlife Biology, EarlyView.
Obtaining accurate information on demographic states, such as the age and sex classes of animals, is an important step for monitoring wildlife populations. Traditionally, demographic data are collected from harvest, aerial surveys and telemetry studies.
Alexej P. K. Sirén, Zalmai Moheb, Emily Carlson, Adam Beiner, Michael Pfeister, Ian Doherty, Bruno López Del Hierro, Claudio López Del Hierro, Isabella Hanley, Helena Firlings, Ela Shevket, Arielle Lifton‐Jochnowitz, Lee Kantar, Scott R. McLellan, Carter Barthelman, Tammy L. Wilson   +15 more
wiley   +1 more source

Number of fields on the Pareto front for each quality and random effect combination.

Number of fields on the Pareto front for each quality and random effect combination.
Wouter Duivesteijn (17867190), Puck J. A. M. Mulders (17867187), Edwin R. van den Heuvel (7937966), Pytrik Reidsma (4003694)   +3 more
core   +1 more source

Label Setting algorithm with Dynamic update of Pareto Front

, 2016
International audienceTo compute short paths on road networks in a cycling context, several criteria can be used like the distance, the security or the attractiveness of a route. Thus, a Multi Objective Shortest Path problem should be solved.
Giret, Antoine, Kergosien, Yannick, Neron, Emmanuel, Sauvanet, Gaël   +3 more
core   +1 more source

Combining kernelised autoencoding and centroid prediction for dynamic multi‐objective optimisation

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Evolutionary algorithms face significant challenges when dealing with dynamic multi‐objective optimisation because Pareto optimal solutions and/or Pareto optimal fronts change. The authors propose a unified paradigm, which combines the kernelised autoncoding evolutionary search and the centroid‐based prediction (denoted by KAEP), for solving ...
Zhanglu Hou, Juan Zou, Gan Ruan, Yuan Liu, Yizhang Xia   +4 more
wiley   +1 more source

Autonomous reaction Pareto-front mapping with a self-driving catalysis laboratory

Nature Chemical Engineering
J. A. Bennett, N. Orouji, M. Khan, S. Sadeghi, J. Rodgers, M. Abolhasani   +5 more
semanticscholar   +1 more source

MOEA/D‐based multi‐row facility layout optimisation method with discontinuity perceiving of the Pareto front

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract In a multi‐row facility layout problem (MRFLP), facilities are arranged in more than one row under the limited layout area. Considering different layout factors, various extensions of MRFLP have been modelled. However, the orientation of input/output (I/O) point in a facility, as a key factor that plays a direct impact on flow cost, is seldom ...
Yinan Guo, Yao Huang, Jian Cheng, Xiaoyu Ban, Xiaoxiao Zhao, Tianyu Liu   +5 more
wiley   +1 more source

Evolutionary Dynamic Multiobjective Optimisation Assisted by Inverse Regression Tree Predictor

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Dynamic multiobjective optimisation problems (DMOPs) are optimisation problems with multiple conflicting objectives that can change over time. Most dynamic multiobjective optimisation evolutionary algorithms (DMOEAs) attempt to estimate Pareto‐optimal sets (PS) directly in the decision space.
Kai Gao, Lihong Xu
wiley   +1 more source

On the Pareto Type III distribution [PDF]

This short note analyzes the distributional properties of Pareto Type III random variables. We introduce a three parameters version of the orignal two parameters distribution proposed by Pareto and derive both the density and the characteristic function.
Giulio Bottazzi
core  

Finding the Pareto front for high-entropy-alloy catalysts. [PDF]

Chem Sci
Zhang C, Lu R, Sun Q, Mao Y, Söhnel T, Zhao Y, Truhlar DG, Wang Z.   +7 more
europepmc   +1 more source