Erhan, H. E.Sav, SinemKalashnikov, S.Tsang, H. H.2018-04-122018-04-122016-07http://hdl.handle.net/11693/37479Date of Conference: 24-29 July 2016Conference name: IEEE Congress on Evolutionary Computation (CEC), 2016RNA structures are important for many biological processes in the cell. One important function of RNA are as catalytic elements. Ribozymes are RNA sequences that fold to form active structures that catalyze important chemical reactions. The folded structure for these RNA are very important; only specific conformations maintain these active structures, so it is very important for RNA to fold in a specific way. The RNA design problem describes the prediction of an RNA sequence that will fold into a given RNA structure. Solving this problem allows researchers to design RNA; they can decide on what folded secondary structure is required to accomplish a task, and the algorithm will give them a primary sequence to assemble. However, there are far too many possible primary sequence combinations to test sequentially to see if they would fold into the structure. Therefore we must employ heuristics algorithms to attempt to solve this problem. This paper introduces SIMARD, an evolutionary algorithm that uses an optimization technique called simulated annealing to solve the RNA design problem. We analyzes three different cooling schedules for the annealing process: 1) An adaptive cooling schedule, 2) a geometric cooling schedule, and 3) a geometric cooling schedule with warm up. Our results show that an adaptive annealing schedule may not be more effective at minimizing the Hamming distance between the target structure and our folded sequence's structure when compared with geometric schedules. The results also show that warming up in a geometric cooling schedule may be useful for optimizing SIMARD. © 2016 IEEE.EnglishAnnealingCoolingEvolutionary algorithmsGeometryHamming distanceOptimizationProblem solvingRNASimulated annealingAdaptive cooling scheduleBiological processCatalytic elementFolded structuresHeuristics algorithmOptimization techniquesPrimary sequencesSecondary structuresBioinformaticsConference Paper10.1109/CEC.2016.7743937