Abstract
Motif discovery is an important problem in bio-informatics that involves the search for approximate matches. Various algorithms have been proposed, including exhaustive searches as well as heuristic searches that involve searching only a subset of all the possible solutions. One such often employed method is the genetic algorithm. A genetic algorithm based approach is employed in MDGA, using a single population. We build on that method using multiple populations, each evolving against different fitness landscapes. Individuals in each population compete for participation in the genetic events of crossover and mutation based on probabilities. Each of these fitness landscapes, is designed to solve a subset of the problem, thus optimizing a particular characteristic. Once evolution in each of these populations has saturated, they are merged according to a global fitness scheme and then evolved. This process continues till the final population also converges. Different options for implementation are also mentioned. We then proceed to compare our results with that of standard methods, on well known datasets and the results obtained are good.
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Venugopal, K.R., Srinivasa, K.G., Patnaik, L.M. (2009). Merge Based Genetic Algorithm for Motif Discovery. In: Soft Computing for Data Mining Applications. Studies in Computational Intelligence, vol 190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00193-2_18
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DOI: https://doi.org/10.1007/978-3-642-00193-2_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00192-5
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