Genetic algorithms are effective to solve many practical problems, but in some cases, they may take a long time to reach an acceptable solution. GAs are easy to implement on parallel computers, and indeed, parallel GAs are popular, but they are controlled by many parameters that are not well understood. The purpose of this book is to explore the effects of the parameters on the search quality and efficiency of parallel GAs, and provide guidelines on how to choose appropriate values for a particular situation.

This chapter presented a brief description of GAs and some concepts that will be used in the remainder of the book. ‘In particular, the next chapter uses the concepts of partitions and schemata to develop a model that relates the quality of the solution reached by a simple G A with the size of its population. This chapter also outlined the different types of parallel GAs that are explored in the rest of the book.


Genetic Algorithm Parallel Genetic Algorithm Slave Processor Search Quality Good Trait 
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 Science+Business Media New York 2001

Authors and Affiliations

  • Erick Cantú-Paz
    • 1
  1. 1.Lawrence Livermore National LabUSA

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