论文标题
从2015年起，生物信息学中遗传算法的流行率摘要
A summary of the prevalence of Genetic Algorithms in Bioinformatics from 2015 onwards
论文作者
论文摘要
近年来，机器学习在各种领域，尤其是在医疗保健和生物信息学方面的存在越来越多。更具体地说，机器学习算法发现大多数应用的领域是遗传算法。本文的目的是对2015年发表的文章进行调查，从而与遗传算法（ga）（ga）（ga）进行了调查。进行了使用Google Scholar以及Publish或Perish以及Scimago Journal＆Countryrank的使用，以寻找可观的来源。在分析了来自生物信息学领域的31篇文章后，很明显，遗传算法很少形成完整的应用，相反，它们依靠其他重要算法，例如支持向量机器。但是，尽管这种算法的使用促成了GA程序对准确性的重点，但在此过程中，它通常会放在计算时间。实际上，大多数使用GAS进行分类和特征选择的应用程序接近或以100％的成功率进行的应用，未来GA开发的重点应针对其他地方。基于人群的搜索（例如GA）通常与其他机器学习算法结合使用。在此范围审查中，发现遗传算法与支持向量机的遗传算法表现最好。经常评估的性能指标是准确性。测量精度避免了测量气体的主要弱点，即计算时间。遗传算法的未来可能是开放式进化算法，该算法试图提高复杂性并找到多样化的解决方案，而不是优化健身函数并从初始解决方案群体中融合到单个最佳解决方案。
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.
