Downloads provided by UsageCounts
doi: 10.1111/itor.13164
handle: 10609/146992
AbstractMetaheuristic algorithms are employed to solve complex and large‐scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics.
metaheuristics, metaheurísticas, Combinatorial optimization, Bioinformatics, ESTADISTICA E INVESTIGACION OPERATIVA, 006, bioinformatics, Metaheuristics, optimización combinatoria, bioinformática, optimització combinatòria, metaheuristiques, combinatorial optimization, bioinformàtica, Operations research, mathematical programming
metaheuristics, metaheurísticas, Combinatorial optimization, Bioinformatics, ESTADISTICA E INVESTIGACION OPERATIVA, 006, bioinformatics, Metaheuristics, optimización combinatoria, bioinformática, optimització combinatòria, metaheuristiques, combinatorial optimization, bioinformàtica, Operations research, mathematical programming
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 22 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
| views | 44 | |
| downloads | 95 |

Views provided by UsageCounts
Downloads provided by UsageCounts