Making Time for Building Blocks

  • David E. Goldberg
Part of the Genetic Algorithms and Evolutionary Computation book series (GENA, volume 7)


The schema theorem provides one important constraint on the design of a genetic algorithm. Certainly, if the building blocks that make up desirable solutions do not grow in market share, there is little hope that recombination mixes them to form good solutions. Having said this, the schema theorem is something of a static condition saying little about the time to substantial convergence, and understanding time is critical for two reasons. Most pragmatically, understanding time is one leg of a three-legged analytical stool of understanding (1) time or run duration, (2) space or population size, and (3) accuracy or solution quality that enables us to start to estimate solution time complexity, and we certainly are most interested in understanding how selectorecombinative GAs scale as problem size or difficulty change. Second, applicable models of time enable us to analyze other key phenomena by appealing to time-scales arguments such as the race alluded to in chapter 3. In this chapter, we examine an array of little models that help us understand how long it takes before populations converge under a variety of assumptions.


Truncation Selection Convergence Time Drift Time Average Fitness Tournament Selection 
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 Dordrecht 2002

Authors and Affiliations

  • David E. Goldberg
    • 1
  1. 1.University of Illinois at Urbana-ChampaignUSA

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