A study of different annealing schedules in SARNA-predict A permutation based SA algorithm for RNA folding

Purpose–The purpose of this paper is to present a study of the effect of different types of annealing schedules for a ribonucleic acid(RNA)secondary structure prediction algorithm based on simulated annealing(SA).Design/methodology/approach–An RNA folding algorithm was implemented that assembles the final structure from potential substructures(helixes).Structures are encoded as a permutation of helixes.An SA searches this space of permutations.Parameters and annealing schedules were studied and fine-tuned to optimize algorithm performance.Findings–In comparing with mfold,the SA algorithm shows comparable results(in terms of F-measure)even with a less sophisticated thermodynamic model.In terms of average specificity,the SA algorithm has provided surpassing results.Research limitations/implications–Most of the underlying thermodynamic models are too simplistic and incomplete to accurately model the free energy for larger structures.This is the largest limitation of free energy-based RNA folding algorithms in general.Practical implications–The algorithm offers a different approach that can be used in practice to fold RNA sequences quickly.Originality/value–The algorithm is one of only two SA-based RNA folding algorithms.The authors use a very different encoding,based on permutation of candidate helixes.The in depth study of annealing schedules and other parameters makes the algorithm a strong contender.Another benefit is that new thermodynamic models can be incorporated with relative ease(which is not the case for algorithms based on dynamic programming).

Diploidy in evolutionary algorithms for dynamic optimization problems A best-chromosome-wins dominance mechanism

Purpose–In this work,the authors show the performance of the proposed diploid scheme(a representation where each individual contains two genotypes)with respect to two dynamic optimization problems,while addressing drawbacks the authors have identified in previous works which compare diploid evolutionary algorithms(EAs)to standard EAs.The paper aims to discuss this issue.Design/methodology/approach–In the proposed diploid representation of EA,each individual possesses two copies of the genotype.In order to convert this pair of genotypes to a single phenotype,each genotype is individually evaluated in relation to the fitness function and the best genotype is presented as the phenotype.In order to provide a fair and objective comparison,the authors make sure to compare populations which contain the same amount of genetic information,where the only difference is the arrangement and interpretation of the information.The two representations are compared using two shifting fitness functions which change at regular intervals to displace the global optimum to a new position.Findings–For small fitness landscapes the haploid(standard)and diploid algorithms perform comparably and are able to find the global optimum very quickly.However,as the search space increases,rediscovering the global optimum becomes more difficult and the diploid algorithm outperforms the haploid algorithm with respect to how fast it relocates the new optimum.Since both algorithms use the same amount of genetic information,it is only fair to conclude it is the unique arrangement of the diploid algorithm that allows it to explore the search space better.Originality/value–The diploid representation presented here is novel in that instead of adopting a dominance scheme for each allele(value)in the vector of values that is the genotype,dominance is adopted across the entire genotype in relation to its homologue.As a result,this representation can be extended across any alphabet,for any optimization function.