Make Fast Evolutionary Programming Robust by Search Step Control

  • Yong Liu
  • Xin Yao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)


It has been found that both evolutionary programming (EP) and fast EP (FEP) could get stuck in local optima on some test functions. Although a number of methods have been developed to solve this problem, nearly all have focused on how to adjust search step sizes. This paper shows that it is not enough to change the step sizes alone. Besides step control, the shape of search space should be changed so that the search could be driven to other unexplored regions without getting stuck in the local optima. A two-level FEP with deletion is proposed in this paper to make FEP robust on finding better solutions in function optimisation. A coarse-grained search in the upper level could lead FEP to generate a diverse population, while a fine-grained search in the lower level would help FEP quickly find a local optimum in a region. After FEP could not make any progress after falling in a local optimum, deletion would be applied to change the search space so that FEP could start a new fine-grained search from the points generated by the coarse-grained search.


Local Optimum Global Minimum Search Point Search Step Attraction Basin 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yong Liu
    • 1
  • Xin Yao
    • 2
  1. 1.School of Computer ScienceChina University of GeosciencesWuhanP.R. China
  2. 2.School of Computer ScienceThe University of BirminghamEdgbastonU.K.

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