1 research outputs found
A summary of the prevalence of Genetic Algorithms in Bioinformatics from 2015 onwards
In recent years, machine learning has seen an increasing presencein a large
variety of fields, especially in health care and bioinformatics.More
specifically, the field where machine learning algorithms have found most
applications is Genetic Algorithms.The objective of this paper is to conduct a
survey of articles published from 2015 onwards that deal with Genetic
Algorithms(GA) and how they are used in bioinformatics.To achieve the
objective, a scoping review was conducted that utilized Google Scholar
alongside Publish or Perish and the Scimago Journal & CountryRank to search for
respectable sources. Upon analyzing 31 articles from the field of
bioinformatics, it became apparent that genetic algorithms rarely form a full
application, instead they rely on other vital algorithms such as support vector
machines.Indeed, support vector machines were the most prevalent algorithms
used alongside genetic algorithms; however, while the usage of such algorithms
contributes to the heavy focus on accuracy by GA programs, it often sidelines
computation times in the process. In fact, most applications employing GAs for
classification and feature selectionare nearing or at 100% success rate, and
the focus of future GA development should be directed elsewhere.
Population-based searches, like GA, are often combined with other machine
learning algorithms. In this scoping review, genetic algorithms combined with
Support Vector Machines were found to perform best. The performance metric that
was evaluated most often was accuracy. Measuring the accuracy avoids measuring
the main weakness of GAs, which is computational time. The future of genetic
algorithms could be open-ended evolutionary algorithms, which attempt to
increase complexity and find diverse solutions, rather than optimize a fitness
function and converge to a single best solution from the initial population of
solutions.Comment: 20 pages and 2 figure