A genetic algorithm (GA) is a method for solving both constrained and unconstrained optimization problems based on a natural selection process that mimics biological evolution. The algorithm repeatedly modifies a population of individual solutions.
At each step, the genetic algorithm randomly selects individuals from the current population and uses them as parents to produce the children for the next generation. Over successive generations, the population "evolves" toward an optimal solution (MathWorks, 2017).
Speciation Simulator 2.0 is a simple web-based computational biology application designed to mimic patterns of biological evolution. In nature, new species arise and apdapt as a result of natural selection acting upon a gradual accumulation of spontaneous genetic mutations over successive generations. Mutations to the sexual reproduction cells of an organism are most important in terms of biological evolution. These are the types of cells that are necessary for changes to occur and play a vital role in heredity. Passed from one generation to the next, germ-line mutations create tiny changes to the phenotype of an organism. Distinct phenotypes become widely apparent within a population (or a gene pool) when a particular mutation(s) establishes an advantageous trait toward survival, fitness or reproduction. The new phenotype can then become a dominant feature; outnumbering the original variety.
Of course, it is very difficult to precisely simulate or reconstruct the natural mechanisms and evolutionary pathways that result in new species. This application is simply intended to provide a basic demonstration of some of the processes at work in nature; not depicting any real-world instances. The goal of Speciation Simulator 2.0 is to propagate a better understanding for biological evolution, and how new populations of species can emerge and diversify in natural settings.