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Author (up) David Roche; Debora Gil; Jesus Giraldo edit   pdf
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  Title Detecting loss of diversity for an efficient termination of EAs Type Conference Article
  Year 2013 Publication 15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing Abbreviated Journal  
  Volume Issue Pages 561 - 566  
  Keywords EA termination; EA population diversity; EA steady state  
  Abstract Termination of Evolutionary Algorithms (EA) at its steady state so that useless iterations are not performed is a main point for its efficient application to black-box problems. Many EA algorithms evolve while there is still diversity in their population and, thus, they could be terminated by analyzing the behavior some measures of EA population diversity. This paper presents a numeric approximation to steady states that can be used to detect the moment EA population has lost its diversity for EA termination. Our condition has been applied to 3 EA paradigms based on diversity and a selection of functions
covering the properties most relevant for EA convergence.
Experiments show that our condition works regardless of the search space dimension and function landscape.		  
  Address Timisoara; Rumania;  
  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 978-1-4799-3035-7 Medium  
  Area Expedition Conference SYNASC  
  Notes IAM; 600.044; 600.060; 605.203 Approved no  
  Call Number Admin @ si @ RGG2013c Serial 2299  
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