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Original scientific paper

The Island Model Genetic Algorithm: On Separability, Population Size and Convergence

Darrell Whitley ; Department of Computer Science, Colorado State University, Fort Collins. Colorado, USA
Soraya Rana ; Department of Computer Science, Colorado State University, Fort Collins. Colorado, USA
Robert B. Heckendorn ; Department of Computer Science, Colorado State University, Fort Collins. Colorado, USA

Fulltext: english, pdf (7 MB) pages 33-47 downloads: 191* cite
APA 6th Edition
Whitley, D., Rana, S. & Heckendorn, R.B. (1999). The Island Model Genetic Algorithm: On Separability, Population Size and Convergence. Journal of computing and information technology, 7 (1), 33-47. Retrieved from https://hrcak.srce.hr/150198
MLA 8th Edition
Whitley, Darrell, et al. "The Island Model Genetic Algorithm: On Separability, Population Size and Convergence." Journal of computing and information technology, vol. 7, no. 1, 1999, pp. 33-47. https://hrcak.srce.hr/150198. Accessed 22 Nov. 2019.
Chicago 17th Edition
Whitley, Darrell, Soraya Rana and Robert B. Heckendorn. "The Island Model Genetic Algorithm: On Separability, Population Size and Convergence." Journal of computing and information technology 7, no. 1 (1999): 33-47. https://hrcak.srce.hr/150198
Harvard
Whitley, D., Rana, S., and Heckendorn, R.B. (1999). 'The Island Model Genetic Algorithm: On Separability, Population Size and Convergence', Journal of computing and information technology, 7(1), pp. 33-47. Available at: https://hrcak.srce.hr/150198 (Accessed 22 November 2019)
Vancouver
Whitley D, Rana S, Heckendorn RB. The Island Model Genetic Algorithm: On Separability, Population Size and Convergence. Journal of computing and information technology [Internet]. 1999 [cited 2019 November 22];7(1):33-47. Available from: https://hrcak.srce.hr/150198
IEEE
D. Whitley, S. Rana and R.B. Heckendorn, "The Island Model Genetic Algorithm: On Separability, Population Size and Convergence", Journal of computing and information technology, vol.7, no. 1, pp. 33-47, 1999. [Online]. Available: https://hrcak.srce.hr/150198. [Accessed: 22 November 2019]

Abstracts
Parallel Genetic Algorithms have often been reported to yield better performance than Genetic Algorithms which use a single large panmictic population. In the case of the Island Model genetic algorithm, it has been informally argued that having multiple subpopulations helps to preserve genetic diversity, since each island can potentially follow a different search trajectory through the search space. It is also possible that since linearly separable problems are often used to test Genetic Algorithms, that Island Models may simply be particularly well suited to exploiting the separable nature of the test problems. We explore this possibility by using the infinite population models of simple genetic algorithms to study how Island Models can track multiple search trajectories. We also introduce a simple model for better understanding when Island Model genetic algorithms may have an advantage when processing some test problems. We provide empirical results for both linearly separable and nonseparable parameter optimization functions.

Keywords
genetic algorithms

Hrčak ID: 150198

URI
https://hrcak.srce.hr/150198

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