Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Greedy closure evolutionary algorithms
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Hardware acceleration of multi-deme genetic algorithm for the application of DNA codeword searching
Proceedings of the 9th annual conference on Genetic and evolutionary computation
DNA error correcting codes: no crossover
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
Evolutionary approaches to the generation of optimal error correcting codes
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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Error correcting codes over the DNA alphabet are used as embeddable biomarkers. Error correction provides resilience of identification in spite of sequencing errors. Ring optimization is a type of spatially structured evolutionary algorithm derived from models of ring species in nature. This paper compares the performance of a ring optimizer with a standard evolutionary algorithm at searching for large edit metric codes of a given length and able to correct a specified number of errors. The algorithm also incorporates a novel variation operator called Conway crossover that uses Conway's lexicode algorithm as the basis for a binary variation operator. The ring optimizer is found to yield substantially inferior performance. A new type of statistic, last time of innovation is defined and used to compare the two algorithms. Several improvements to the table of best known edit metric codes are presented. Both algorithms yielded improvements to the table, but the improvements due to the ring optimizer were never better than those located by the standard algorithm and worse half of the time.