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In recent years, machine learning has seen an increasing presencein a largevariety of fields, especially in health care and bioinformatics.Morespecifically, the field where machine learning algorithms have found mostapplications is Genetic Algorithms.The objective of this paper is to conduct asurvey of articles published from 2015 onwards that deal with GeneticAlgorithms(GA) and how they are used in bioinformatics.To achieve theobjective, a scoping review was conducted that utilized Google Scholaralongside Publish or Perish and the Scimago Journal & CountryRank to search forrespectable sources. Upon analyzing 31 articles from the field ofbioinformatics, it became apparent that genetic algorithms rarely form a fullapplication, instead they rely on other vital algorithms such as support vectormachines.Indeed, support vector machines were the most prevalent algorithmsused alongside genetic algorithms; however, while the usage of such algorithmscontributes to the heavy focus on accuracy by GA programs, it often sidelinescomputation times in the process. In fact, most applications employing GAs forclassification and feature selectionare nearing or at 100% success rate, andthe focus of future GA development should be directed elsewhere.Population-based searches, like GA, are often combined with other machinelearning algorithms. In this scoping review, genetic algorithms combined withSupport Vector Machines were found to perform best. The performance metric thatwas evaluated most often was accuracy. Measuring the accuracy avoids measuringthe main weakness of GAs, which is computational time. The future of geneticalgorithms could be open-ended evolutionary algorithms, which attempt toincrease complexity and find diverse solutions, rather than optimize a fitnessfunction and converge to a single best solution from the initial population ofsolutions.

A summary of the prevalence of Genetic Algorithms in Bioinformatics from 2015 onwards