5 CONCLUSIONS
The Cultural Algorithm is a stochastic
optimization method that uses evolutionary
algorithmic mechanisms to model cultural
evolution and social behaviors. Just as cultural
evolution contributes to the adaptability of human
society, CA provides an additional degree of
adaptability to evolutionary computation. In this
paper we have introduced the social fabric
influence function in the Cultural Algorithms
framework. This influence function is used to
produce population and knowledge swarms that
are used to optimally solve nonlinearly constrained
optimization problems. The SF metaphor allows
the knowledge sources to distribute their influence
through a social network. We apply this approach
to a set of well-known nonlinearly constrained
optimization problems. It turns out that the used
topology, frequency of distribution of influence,
and conflict resolution play an important role in
how efficiently the system produces knowledge
and population swarms that represent structural
patterns to solve problems.
ACKNOWLEDGEMENTS
Our thanks to Rose Ziad from Yarmouk University
Talal Ali from Wayne State University, and Imad
momani for help in developing the current design
and infrastructure of the new influence function.
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