Applications of evolution

Antibiotic resistance can be a result of point mutations in the pathogen genome at a rate of about 1 in 10⁸ per chromosomal replication. The antibiotic action against the pathogen can be seen as an environmental pressure; those bacteria which have a mutation allowing them to survive will live on to reproduce. They will then pass this trait to their offspring, which will result in a fully resistant colony.

Understanding the changes that have occurred during organism's evolution can reveal the genes needed to construct parts of the body, genes which may be involved in human genetic disorders.^[6] For example, the Mexican tetra is an albino cavefish that lost its eyesight during evolution. Breeding together different populations of this blind fish produced some offspring with functional eyes, since different mutations had occurred in the isolated populations that had evolved in different caves.^[7] This helped identify genes required for vision and pigmentation, such as crystallins and the melanocortin 1 receptor.^[8] Similarly, comparing the genome of the Antarctic icefish, which lacks red blood cells, to close relatives such as the Antarctic rockcod revealed genes needed to make these blood cells.^[9]

As evolution can produce highly optimised processes and networks, it has many applications in computer science. Here, simulations of evolution using evolutionary algorithms and artificial life started with the work of Nils Aall Barricelli in the 1960s, and was extended by Alex Fraser, who published a series of papers on simulation of artificial selection.^[10] Artificial evolution became a widely recognised optimisation method as a result of the work of Ingo Rechenberg in the 1960s and early 1970s, who used evolution strategies to solve complex engineering problems.^[11] Genetic algorithms in particular became popular through the writing of John Holland.^[12] As academic interest grew, dramatic increases in the power of computers allowed practical applications, including the automatic evolution of computer programs.^[13] Evolutionary algorithms are now used to solve multi-dimensional problems more efficiently than software produced by human designers, and also to optimise the design of systems.^[14]