Discovery of Emergent Sorting Behavior using Swarm Intelligence and Grid-Enabled Genetic Algorithms

Discovery of Emergent Sorting Behavior using Swarm Intelligence and Grid-Enabled Genetic Algorithms

Dimitris Kalles, Alexis Kaporis, Vassiliki Mperoukli, Anthony Chatzinouskas
ISBN13: 9781466660786|ISBN10: 1466660783|EISBN13: 9781466660793
DOI: 10.4018/978-1-4666-6078-6.ch012
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MLA

Kalles, Dimitris, et al. "Discovery of Emergent Sorting Behavior using Swarm Intelligence and Grid-Enabled Genetic Algorithms." Biologically-Inspired Techniques for Knowledge Discovery and Data Mining, edited by Shafiq Alam, et al., IGI Global, 2014, pp. 249-271. https://doi.org/10.4018/978-1-4666-6078-6.ch012

APA

Kalles, D., Kaporis, A., Mperoukli, V., & Chatzinouskas, A. (2014). Discovery of Emergent Sorting Behavior using Swarm Intelligence and Grid-Enabled Genetic Algorithms. In S. Alam, G. Dobbie, Y. Koh, & S. ur Rehman (Eds.), Biologically-Inspired Techniques for Knowledge Discovery and Data Mining (pp. 249-271). IGI Global. https://doi.org/10.4018/978-1-4666-6078-6.ch012

Chicago

Kalles, Dimitris, et al. "Discovery of Emergent Sorting Behavior using Swarm Intelligence and Grid-Enabled Genetic Algorithms." In Biologically-Inspired Techniques for Knowledge Discovery and Data Mining, edited by Shafiq Alam, et al., 249-271. Hershey, PA: IGI Global, 2014. https://doi.org/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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