Multi-layered Niche Formation

Conference paper


Recently an abstraction of genetic algorithms has been developed in which a population of GAs in any epoch is represented by a single vector whose elements are the the probabilities of the corresponding bit positions being equivalent to 1. The process of evolution is represented by learning the elements of the probability vector. We have previously extended this to model homeotic genes which are environmentally driven and turn other genes on and off. In this paper we incrementally develop the algorithm on a set of standard problems used to compare methods for the simultaneous optimisation of conflicting criteria within a single population.


Genetic Algorithm Learning Rate Probability Vector Realisable Gene Homeotic Gene 
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 Wien 1998

Authors and Affiliations

  • C. Fyfe
    • 1
  1. 1.Department of Computing and Information SystemsThe University of PaisleyPaisleyUK

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