Binary or Float?

  • Zbigniew Michalewicz


As discussed in the previous chapter, there are some problems that GA applications encounter that sometimes delay, if not prohibit, finding the optimal solutions with the desired precision. One of the implications of these problems was premature convergence of the entire population to a non—global optimum (Chapter 4); other consequences include inability to perform fine local tuning and inability to operate in the presence of nontrivial constraints (Chapters 6 and 7).


Genetic Algorithm Binary Representation Floating Point Gray Code Random Digit 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Zbigniew Michalewicz
    • 1
  1. 1.Department of Computer ScienceUniversity of North CarolinaCharlotteUSA

Personalised recommendations