A Genetic Algorithm using Order Crossover for RNA Folding

K.C. Wiese and E. Glen (Canada)


AI and Bioinformatics, Evolutionary Computation, Genetic Algorithms, Search, Optimization, Genetic Operators, Se lection, Biological Modeling


This paper presents a permutation based genetic algorithm (GA) using order crossover (OX) to predict the secondary structure of RNA molecules, a problem also known as RNA folding. More specifically, the proposed algorithm predicts which individual canonical base pairs will form hydrogen bonds and build helices, also known as stem loops. Since RNA is involved in both transcription and translation and also has catalytic and structural roles in the cell, knowing its structure is of fundamental importance since it will deter mine the function of the RNA molecule. We introduce a GA where a permutation is used to encode the secondary struc ture of RNA molecules. We discuss initial results on RNA sequences of lengths 76, 681 and 785 nucleotides and pre sent several improvements to the algorithm.

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