Analysis of a triploid genetic algorithm over deceptive landscapes

Li Meng, Seamus Hill, Colm O'Riordan

Research output: Chapter in Book or Conference Publication/ProceedingConference Publicationpeer-review

Abstract

This paper compares the performance of a canonical genetic algorithm (CGA) against that of the triploid genetic algorithm (TGA) introduced in [10], over a number of well known deceptive landscapes in order to increase our understanding of the TGA's ability to control convergence. The TGA incorporates a mechanism to control the convergence direction instead of simply increasing the population diversity. Results indicate that the TGA appears to have the highest level of difficulty in solving problems with a disordered pattern. While the disorder-mapping seems to improve the CGA's performance, it has a negative effect on the performance of the TGA. However, the results illustrate that the TGA performs better on problems with epistasis present.

Original languageEnglish
Title of host publication27th Annual ACM Symposium on Applied Computing, SAC 2012
Pages244-249
Number of pages6
DOIs
Publication statusPublished - 2012
Event27th Annual ACM Symposium on Applied Computing, SAC 2012 - Trento, Italy
Duration: 26 Mar 201230 Mar 2012

Publication series

NameProceedings of the ACM Symposium on Applied Computing

Conference

Conference27th Annual ACM Symposium on Applied Computing, SAC 2012
Country/TerritoryItaly
CityTrento
Period26/03/1230/03/12

Keywords

  • diversity
  • genetic algorithms

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