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. 2014 Jul 5;15:234. doi: 10.1186/1471-2105-15-234

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Copyright © 2014 Jayaseelan and Steinbeck; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Evolutionary algorithm scheme for CASE. Evolution starts with an initial population of 16 individuals. The initial population is seeded by mutating the single random structure generated from the molecular formula. The population is evaluated for termination criteria, i.e. maximum fitness, maximum allowed runtime, maximum allowed generations, or maximum allowed generations with no improvement. Evolution continues until any one of the above conditions is met. The population is doubled by mutating every individual before fitness is evaluated. After fitness evaluation, the fittest ones are promoted to the next generation by round-robin tournaments selection. Once one of the termination conditions is met, the solution set is reported.