Abstract
On the face of it, the previous chapter would seem like good news. After all, solution accuracy and reliability were predictably controlled using nothing more than appropriate (sub- or near-linear) population sizing. Moreover, this happy circumstance appeared to occur on both easy and hard problems. But closer scrutiny of the presented results shows that all is not necessarily well. In the last chapter, and in previous works on population sizing, when difficult problems were tested, tight linkage was assumed. That is, alleles contributing to a difficult building block were assumed to be physically close to one another, and crossover operators such as single-point crossover were used to facilitate the necessary exchange of intact building blocks with high probability.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Goldberg, D.E. (2002). Mixing, Control Maps, and Genetic Algorithm Success. In: The Design of Innovation. Genetic Algorithms and Evolutionary Computation, vol 7. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-3643-4_11
Download citation
DOI: https://doi.org/10.1007/978-1-4757-3643-4_11
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4757-3645-8
Online ISBN: 978-1-4757-3643-4
eBook Packages: Springer Book Archive