Using regression to improve local convergence

  title={Using regression to improve local convergence},
  author={Stefan Bird and Xiaodong Li},
  journal={2007 IEEE Congress on Evolutionary Computation},
Traditionally Evolutionary Algorithms (EAs) choose candidate solutions based on their individual fitnesses, usually without directly looking for patterns in the fitness landscape discovered. These patterns often contain useful information that could be used to guide the EA to the optimum. While an EA is able to quickly locate the general area of a peak, it can take a considerable amount of time to refine the solution to accurately reflect its true location. We present a new technique that can… CONTINUE READING

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Publications referenced by this paper.
Showing 1-10 of 11 references

Enhancing the robustness of a speciation-based PSO

2006 IEEE International Conference on Evolutionary Computation • 2006
View 1 Excerpt

Multiswarms, exclusion, and anti-convergence in dynamic environments

IEEE Transactions on Evolutionary Computation • 2006
View 1 Excerpt

A particle swarm model for tracking multiple peaks in a dynamic environment using speciation

Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753) • 2004
View 2 Excerpts

A new locally convergent particle swarm optimiser

F. van den Bergh, A. Engelbrecht
Proceedings of the 2002 IEEE International Conference on Systems, Man and Cybernetics, 2002. 2007 IEEE Congress on Evolutionary Computation (CEC 2007) 599 • 2002
View 1 Excerpt

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