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Discovery of Emergent Sorting Behavior using Swarm Intelligence and Grid-Enabled Genetic Algorithms
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Author(s): Dimitris Kalles (Hellenic Open University, Greece), Alexis Kaporis (University of the Aegean, Greece), Vassiliki Mperoukli (Hellenic Open University, Greece)and Anthony Chatzinouskas (Hellenic Open University, Greece)
Copyright: 2014
Pages: 23
Source title:
Biologically-Inspired Techniques for Knowledge Discovery and Data Mining
Source Author(s)/Editor(s): Shafiq Alam (University of Auckland, New Zealand), Gillian Dobbie (University of Auckland, New Zealand), Yun Sing Koh (University of Auckland, New Zealand)and Saeed ur Rehman (Unitec Institute of Technology, New Zealand)
DOI: 10.4018/978-1-4666-6078-6.ch012
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Abstract
The authors in this chapter use simple local comparison and swap operators and demonstrate that their repeated application ends up in sorted sequences across a range of variants, most of which are also genetically evolved. They experimentally validate a square run-time behavior for emergent sorting, suggesting that not knowing in advance which direction to sort and allowing such direction to emerge imposes a n/logn penalty over conventional techniques. The authors validate the emergent sorting algorithms via genetically searching for the most favorable parameter configuration using a grid infrastructure.
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