Skip to main content
Article thumbnail
Location of Repository

A self-organizing random immigrants genetic algorithm for dynamic optimization problems

By Renato Tinos and Shengxiang Yang

Abstract

This is the authors' final draft of the paper published as Genetic Programming and Evolvable Machines, 2007, 8(3), pp. 255-286. The original publication is available at www.springerlink.com, DOI: 10.1007/s10710-007-9024-zIn this paper a genetic algorithm is proposed where the worst individual and\ud individuals with indices close to its index are replaced in every generation by randomly\ud generated individuals for dynamic optimization problems. In the proposed genetic algorithm, the replacement of an individual can affect other individuals in a chain reaction. The new individuals are preserved in a subpopulation which is defined by the number of individuals created in the current chain reaction. If the values of fitness are similar, as is the case with small diversity, one single replacement can affect a large number of individuals in the population. This simple approach can take the system to a self-organizing behavior, which can be useful to control the diversity level of the population and hence allows the genetic algorithm to escape from local optima once the problem changes due to\ud the dynamics

Publisher: Springer
Year: 2007
DOI identifier: 10.1007/s10710-007-9024-z
OAI identifier: oai:lra.le.ac.uk:2381/1117

Suggested articles

Citations

  1. (1996). A genetic algorithm with variable range of local search for tracking changing environments. doi
  2. (1998). Adaptation to a changing environment by means of the feedback thermodynamical genetic algorithm. doi
  3. (1996). An Introduction to Genetic Algorithms. doi
  4. (1990). An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuouis, time-dependent nonstationary environments.
  5. (1993). Analyzing deception in trap functions. doi
  6. (1986). Biological extinction in earth history. doi
  7. (2004). Constructing dynamic test environments for genetic algorithms based on problem difficulty. doi
  8. (2003). Evolutionary approaches to dynamic optimization problems - introduction and recent trends. doi
  9. (2001). Evolutionary Optimization in Dynamic Environments. doi
  10. (2000). Evolutionary optimization in non-stationary environments. doi
  11. (2005). Evolutionary optimization in uncertain environments - a survey. doi
  12. (2005). Experimental study on population-based incremental learning algorithms for dynamic optimization problems. doi
  13. (2002). Extending particle swarm optimisers with self-organized criticality. doi
  14. (1992). Genetic algorithms for changing environments. doi
  15. (1993). Genetic algorithms for tracking changing environments.
  16. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. doi
  17. (1997). How Nature Works: the Science of Self-organized Criticality. doi
  18. (2003). Non-stationary problem optimization using the primal-dual genetic algorithm. In doi
  19. (1997). On the use of niching for dynamic landscapes. doi
  20. (2003). Optimization with extremal dynamics. doi
  21. (2001). Self-organized criticality and mass extinction in evolutionary algorithms. doi
  22. (1998). Self-organized Criticality: Emergent Complex Behavior in Physical and Biological Systems. doi
  23. (1987). Self-organized criticality. an explanation of 1/f noise. doi
  24. (2002). The Design of Innovation: Lessons from and for Competent Genetic Algorithms. doi
  25. (1993). The Origins of Order: Self-organization and Selection in Evolution. doi
  26. (1989). Wonderful Life: The Burgess Shale and the Nature of History. doi

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