A long-standing challenge to evolutionary theory has been whether it can explain the origin of complex organismal features. We examined this issue using digital organisms—computer programs that self-replicate, mutate, compete and evolve. Populations of digital organisms often evolved the ability to perform complex logic functions requiring the coordinated execution of many genomic instructions. Complex functions evolved by building on simpler functions that had evolved earlier, provided that these were also selectively favoured. However, no particular intermediate stage was essential for evolving (...) complex functions. The first genotypes able to perform complex functions differed from their non-performing parents by only one or two mutations, but differed from the ancestor by many mutations that were also crucial to the new functions. In some cases, mutations that were deleterious when they appeared served as stepping-stones in the evolution of complex features. These findings show how complex functions can originate by random mutation and natural selection. (shrink)

Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them. However, because evolution is an algorithmic process that transcends the substrate in which it occurs, evolution’s creativity is not limited to nature. Indeed, many researchers in the field of digital evolution have observed their evolving algorithms and organisms subverting their intentions, exposing unrecognized bugs in their code, producing unexpected adaptations, or exhibiting outcomes uncannily convergent with ones in nature. Such stories routinely reveal (...) creativity by evolution in these digital worlds, but they rarely fit into the standard scientific narrative. Instead they are often treated as mere obstacles to be overcome, rather than results that warrant study in their own right. The stories themselves are traded among researchers through oral tradition, but that mode of information transmission is inefficient and prone to error and outright loss. Moreover, the fact that these stories tend to be shared only among practitioners means that many natural scientists do not realize how interesting and lifelike digital organisms are and how natural their evolution can be. To our knowledge, no collection of such anecdotes has been published before. This paper is the crowd-sourced product of researchers in the fields of artificial life and evolutionary computation who have provided first-hand accounts of such cases. It thus serves as a written, fact-checked collection of scientifically important and even entertaining stories. In doing so we also present here substantial evidence that the existence and importance of evolutionary surprises extends beyond the natural world, and may indeed be a universal property of all complex evolving systems. (shrink)

Evolution has shaped all living organisms on Earth, although many details of this process are shrouded in time. However, it is possible to see, with one's own eyes, evolution as it happens by performing experiments in defined laboratory conditions with microbes that have suitably fast generations. The longest‐running microbial evolution experiment was started in 1988, at which time twelve populations were founded by the same strain ofEscherichia coli. Since then, the populations have been serially propagated and have evolved for tens (...) of thousands of generations in the same environment. The populations show numerous parallel phenotypic changes, and such parallelism is a hallmark of adaptive evolution. Many genetic targets of natural selection have been identified, revealing a high level of genetic parallelism as well. Beneficial mutations affect all levels of gene regulation in the cells including individual genes and operons all the way to global regulatory networks. Of particular interest, two highly interconnected networks—governing DNA superhelicity and the stringent response—have been demonstrated to be deeply involved in the phenotypic and genetic adaptation of these experimental populations. BioEssays 29:846–860, 2007. © 2007 Wiley Periodicals, Inc. (shrink)