Introducing a Robust and Efficient Stopping Criterion for MOEA's

  • Guerrero Madrid, José Luis
  • Martí, Luis
  • Berlanga de Jesús, Antonio
  • García, Jesús
  • Molina López, José Manuel
Proceedings of: IEEE World Congress on Computational Intelligence 2010 (WCCI 2010): IEEE Congress on Evolutionary Computation (CEC 2010). Barcelona, Spain, 18-23 July 2010., Soft computing methods, and Multi-Objective Evolutionary Algorithms (MOEAs) in particular, lack a general convergence criterion which prevents these algorithms from detecting the generation where further evolution will provide little improvements (or none at all) over the current solution, making them waste computational resources. This paper presents the Least Squares Stopping Criterion (LSSC), an easily configurable and implementable, robust and efficient stopping criterion, based on simple statistical parameters and residue analysis, which tries to introduce as few setup parameters as possible, being them always related to the MOEAs research field rather than the techniques applied by the criterion., This work was supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, CAM CONTEXTS (S2009/TIC- 1485) and DPS2008-07029-C02-02, Publicado