Applications
Further information:
Artificial selection and
Evolutionary computation
Evolutionary biology, and in particular the understanding of how organisms evolve through natural selection, is an area of science with many practical applications.
[258] A major technological application of evolution is
artificial selection, which is the intentional selection of certain traits in a population of organisms. Humans have used artificial selection for thousands of years in the
domestication of plants and animals.
[259] More recently, such selection has become a vital part of
genetic engineering, with
selectable markers such as antibiotic resistance genes being used to manipulate DNA in
molecular biology. It is also possible to use repeated rounds of mutation and selection to evolve proteins with particular properties, such as modified
enzymes or new
antibodies, in a process called
directed evolution.
[260]
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.
[261] 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.
[262] This helped identify genes required for vision and pigmentation, such as
crystallins and the
melanocortin 1 receptor.
[263] Similarly, comparing the genome of the
Antarctic icefish, which lacks
red blood cells, to close relatives such as the
zebrafish revealed genes needed to make these blood cells.
[264]
As evolution can produce highly optimized 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.
[265] Artificial evolution became a widely recognized optimization 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.
[266] Genetic algorithms in particular became popular through the writing of
John Holland.
[267] As academic interest grew, dramatic increases in the power of computers allowed practical applications, including the automatic evolution of computer programs.
[268] Evolutionary algorithms are now used to solve multi-dimensional problems more efficiently than software produced by human designers, and also to optimize the design of systems.
[269]