toggle visibility Search & Display Options

Select All    Deselect All
 |   | 
  Record Links
Author (up) David Roche; Debora Gil; Jesus Giraldo edit   pdf
  Title Using statistical inference for designing termination conditions ensuring convergence of Evolutionary Algorithms Type Conference Article
  Year 2011 Publication 11th European Conference on Artificial Life Abbreviated Journal  
  Volume Issue Pages  
  Abstract A main challenge in Evolutionary Algorithms (EAs) is determining a termination condition ensuring stabilization close to the optimum in real-world applications. Although for known test functions distribution-based quantities are good candidates (as far as suitable parameters are used), in real-world problems an open question still remains unsolved. How can we estimate an upper-bound for the termination condition value ensuring a given accuracy for the (unknown) EA solution?
We claim that the termination problem would be fully solved if we defined a quantity (depending only on the EA output) behaving like the solution accuracy. The open question would be, then, satisfactorily answered if we had a model relating both quantities, since accuracy could be predicted from the alternative quantity. We present a statistical inference framework addressing two topics: checking the correlation between the two quantities and defining a regression model for predicting (at a given confidence level) accuracy values from the EA output.
  Address Paris, France  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ECAL  
  Notes IAM; Approved no  
  Call Number IAM @ iam @ RGG2011b Serial 1678  
Permanent link to this record
Select All    Deselect All
 |   | 

Save Citations:
Export Records: