Chapter I The Role of Simulated Evolutions in Bioinformatics
Abstract:
Bioinformatics is a fast growing field in the scientific community. It involves a wide range of problems, for example, DNA sequence analyses, RNA secondary structure predictions, phylogenetic analyses and microarray analyses (just to name a few). Furthermore, new problem domains are constantly evolving, such as proteomics and biomedical applications. In fact, many of these problems are very complex in nature and are often hard to solve by conventional methods. We have learned that Evolutionary Computing (EC) is a powerful optimization technique that solves complex problems and it has been proven successful in various application domains. Therefore, in this chapter, we introduce EC applications in Bioinformatics. The chapter first gives an overview of some key concepts and classical problems in Bioinformatics. Then, it is followed by two case studies, where the problem’s background, the mathematical formulation, the EC representation and the performance of the methods are presented in details.
Citations
| 18 | A massively parallel genetic algorithm for RNA secondary structure prediction – Shapiro, Navetta - 1994 |
| 3 | A Clustal Alignment Improver using Evolutionary Algorithms – Thomsen, Fogel, et al. - 2002 |
