62,275 research outputs found
Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm
This paper combines the idea of a hierarchical distributed genetic algorithm
with different inter-agent partnering strategies. Cascading clusters of
sub-populations are built from bottom up, with higher-level sub-populations
optimising larger parts of the problem. Hence higher-level sub-populations
search a larger search space with a lower resolution whilst lower-level
sub-populations search a smaller search space with a higher resolution. The
effects of different partner selection schemes for (sub-)fitness evaluation
purposes are examined for two multiple-choice optimisation problems. It is
shown that random partnering strategies perform best by providing better
sampling and more diversity
'On the Application of Hierarchical Coevolutionary Genetic Algorithms: Recombination and Evaluation Partners'
This paper examines the use of a hierarchical coevolutionary genetic algorithm under different partnering strategies. Cascading clusters of sub-populations are built from the bottom up, with higher-level sub-populations optimising larger parts of the problem. Hence higher-level sub-populations potentially search a larger search space with a lower resolution whilst lower-level sub-populations search a smaller search space with a higher resolution. The effects of different partner selection schemes amongst the sub-populations on solution quality are examined for two constrained optimisation problems. We examine a number of recombination partnering strategies in the construction of higher-level individuals and a number of related schemes for evaluating sub-solutions. It is shown that partnering strategies that exploit problem-specific knowledge are superior and can counter inappropriate (sub-) fitness measurements
Efficient methods of automatic calibration for rainfall-runoff modelling in the Floreon+ system
Calibration of rainfall-runoff model parameters is an inseparable part of hydrological simulations. To achieve more accurate results of these simulations, it is necessary to implement an efficient calibration method that provides sufficient refinement of the model parameters in a reasonable time frame. In order to perform the calibration repeatedly for large amount of data and provide results of calibrated model simulations for the flood warning process in a short time, the method also has to be automated. In this paper, several local and global optimization methods are tested for their efficiency. The main goal is to identify the most accurate method for the calibration process that provides accurate results in an operational time frame (typically less than 1 hour) to be used in the flood prediction Floreon(+) system. All calibrations were performed on the measured data during the rainfall events in 2010 in the Moravian-Silesian region (Czech Republic) using our in-house rainfall-runoff model.Web of Science27441339
The Impact of Data Replicatino on Job Scheduling Performance in Hierarchical data Grid
In data-intensive applications data transfer is a primary cause of job
execution delay. Data access time depends on bandwidth. The major bottleneck to
supporting fast data access in Grids is the high latencies of Wide Area
Networks and Internet. Effective scheduling can reduce the amount of data
transferred across the internet by dispatching a job to where the needed data
are present. Another solution is to use a data replication mechanism. Objective
of dynamic replica strategies is reducing file access time which leads to
reducing job runtime. In this paper we develop a job scheduling policy and a
dynamic data replication strategy, called HRS (Hierarchical Replication
Strategy), to improve the data access efficiencies. We study our approach and
evaluate it through simulation. The results show that our algorithm has
improved 12% over the current strategies.Comment: 11 pages, 7 figure
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