UC Riverside

The field of Bioinformatics has advanced rapidly in recent years. Breakthroughs in sequencing technology have driven an explosion in the production of genetic data. As a result, the development of methods to interpret this data accurately and in a timely manner has become a major bottleneck in bioinformatics research. This task is further complicated by imperfect knowledge of genetic features: performance-altering assumptions can easily be introduced during algorithm development, and methodology may quickly become obsolete. Genetic algorithms are an evolution-inspired class of machine learning algorithms that show great promise to resolve these problems. These algorithms gradually refine solutions through natural selection, evolving a solution to a problem in bioinformatics rather than manually designing a search strategy. Due to this learning process determining how features are identified, genetic algorithms do not rely on human knowledge of the problem. Consequently, biases are largely limited to the data used to determine how fitness is evaluated. Genetic algorithms also make better use of computational resources by reducing search space and utilizing parallel computation. In order to examine this potential, this work explores many different implementations of genetic algorithms in bioinformatics and their results.