Description Usage Arguments Details Value References See Also
View source: R/stoppingConditions.R
Online Convergence Detection (OCD) is a technique for detecting convergence of an algorithm based on statistical testing [1]. A two sided ttest as well as a chisquared variance test are performed and serve as stopping criterion if significant.
1 2 
varLimit 

nPreGen 
The number 
maxGen 
The number of iteration that should be spent at the maximum. 
fitnessValue 
The logical parameter 
dispersion 
The logical parameter 
evolutionPath 
The logical parameter 
Basically, two different analyses are performed for detecting convergence.
A statistical chisquared variance test is performed which checks whether the variance of a set of performance indicator values decreases
below a predefined variance limit significantly. Additionally, a two sided ttest is performed in order to check if there is no significant
linear trend of the performance indicator values. The significance level for both tests is fixed with alpha = 0.05.
The algorithm execution is terminated if one of these conditions holds for the last i and second last (i  1) generation. In this implementation,
the performance indicator of interest are: fitnessValue, dispersion, evolutionPath
. A performance indicator value corresponds to the difference
between e.g. the best fitness value of the current generation and that of the last generation. Depending on the number nPreGen
, a vector
PI
of length nPreGen
is computed internally, that stores those differences for each active performance indicator.
stopOnOCD
returns TRUE if the optimizer should terminate the execution or FALSE if not.
[1] Wagner and Trautmann (2009). OCD: Online Convergence Detection for Evolutionary MultiObjective Algorithms Based on Statistical Testing. In Lecture Notes in Computer Science, pp. 198215.
Other stopping.conditions: stopOnCondCov
,
stopOnIndefCovMat
,
stopOnMaxIters
,
stopOnNoEffectAxis
,
stopOnNoEffectCoord
,
stopOnOptParam
,
stopOnOptValue
,
stopOnTimeBudget
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