Skip to main content
Article thumbnail
Location of Repository

Genetic Algorithms with Elite-Based Immigrants for Changing Optimization Problems

By Shengxiang Yang


This is the authors' final draft of the Book Chapter published in Lecture Notes in Computer Science, 2007, vol. 4448, pp.627-636. The final publsihed version is available at

Publisher: Springer Verlag
Year: 2007
OAI identifier:

Suggested articles


  1. (1996). A comparative study of steady state and generational genetic algorithms for use in nonstationary environments. doi
  2. (2000). A multi-population approach to dynamic optimization problems. doi
  3. (2003). An immune system-based genetic algorithm to deal with dynamic environments: diversity and memory. doi
  4. (2002). Dynamic memory model for non-stationary optimization. doi
  5. (2002). Evolutionary Optimization in Dynamic Environments. doi
  6. Experimental study on population-based incremental learning algorithms for dynamic optimization problems. doi
  7. (1992). Genetic algorithms for changing environments. doi
  8. (1993). Genetic algorithms for tracking changing environments.
  9. Locating and tracking multiple dynamic optima by a particle swarm model using speciation. doi
  10. (1999). Memory enhanced evolutionary algorithms for changing optimization problems. doi
  11. Memory-based immigrants for genetic algorithms in dynamic environments. doi
  12. (2003). Non-stationary problem optimization using the primal-dual genetic algorithm. doi
  13. (1987). Nonstationary function optimization using genetic algorithms with dominance and diploidy.
  14. (1999). Searching for optima in non-stationary environments. doi
  15. (1992). The royal road for genetic algorithms: fitness landscapes and GA performance.

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.