Evolution provides a creative fount of complex and subtle adaptations that often surprise the scientists who discover them. However, the creativity of evolution is not limited to the natural world: Artificial organisms evolving in computational environments have also elicited surprise and wonder from the researchers studying them. The process of evolution is an algorithmic process that transcends the substrate in which it occurs. Indeed, many researchers in the field of digital evolution can provide examples of how their evolving algorithms and organisms have creatively subverted their expectations or intentions, exposed unrecognized bugs in their code, produced unexpectedly adaptations, or engaged in behaviors and outcomes, uncannily convergent with ones found in nature. Such stories routinely reveal surprise and 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. Bugs are fixed, experiments are refocused, and one-off surprises are collapsed into a single data point. 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 article 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.

July 01 2006 Life's Solution: Inevitable Humans in a Lonely Universe Thomas S. Ray Thomas S. Ray Search for other works by this author on: This Site Google Scholar Author and Article Information Thomas S. Ray Online Issn: 1530-9185 Print Issn: 1064-5462 © 2006 Massachusetts Institute of Technology2006 Artificial Life (2006) 12 (3): 453–456. https://doi.org/10.1162/artl.2006.12.3.453 Cite Icon Cite Permissions Share Icon Share Twitter LinkedIn Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Thomas S. Ray; Life's Solution: Inevitable Humans in a Lonely Universe. Artif Life 2006; 12 (3): 453–456. doi: https://doi.org/10.1162/artl.2006.12.3.453 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu nav search search input Search input auto suggest search filter All ContentAll JournalsArtificial Life Search Advanced Search This content is only available as a PDF. © 2006 Massachusetts Institute of Technology2006 Article PDF first page preview Close Modal You do not currently have access to this content.

Inspired by the efficient method of locomotion of the rattlesnake Crotalus cerastes, the objective of this work is automatic design through genetic programming (GP) of the fastest possible (sidewinding) locomotion of simulated limbless, wheelless snake-like robot (Snakebot). The realism of simulation is ensured by employing the Open Dynamics Engine (ODE), which facilitates implementation of all physical forces, resulting from the actuators, joints constrains, frictions, gravity, and collisions. Reduction of the search space of the GP is achieved by representation of Snakebot as a system comprising identical morphological segments and by automatic definition of code fragments, shared among (and expressing the correlation between) the evolved dynamics of the vertical and horizontal turning angles of the actuators of Snakebot. Empirically obtained results demonstrate the emergence of sidewinding locomotion from relatively simple motion patterns of morphological segments. Robustness of the sidewinding Snakebot, which is considered to be the ability to retain its velocity when situated in an unanticipated environment, is illustrated by the ease with which Snakebot overcomes various types of obstacles such as a pile of or burial under boxes, rugged terrain, and small walls. The ability of Snakebot to adapt to partial damage by gradually improving its velocity characteristics is discussed. Discovering compensatory locomotion traits, Snakebot recovers completely from single damage and recovers a major extent of its original velocity when more significant damage is inflicted. Exploring the opportunity for automatic design and adaptation of a simulated artifact, this work could be considered as a step toward building real Snakebots, which are able to perform robustly in difficult environments.

The objective of this work is automatic design through genetic programming, of the fastest possible locomotion of simulated snake-like robot (Snakebot). The realism of simulation is ensured by employing the Open Dynamics Engine software library. Empirical results demonstrate the emergence of sidewinding as fastest locomotion gait. Robustness of the sidewinding is illustrated by the ease with which Snakebot overcomes various types of obstacles. The ability of Snakebot to adapt to partial damage by gradually improving its velocity characteristics is shown. Discovering compensatory locomotion traits, Snakebot recovers completely from single damage and recovers a major extent of its original velocity when more significant damage is inflicted.

Different threads of multithreaded digital organisms are found to cluster into tissues which differ in patterns of code execution. Evolution in a network environment has led to the differentiation of new tissues. Some new tissues emerge through a process of gene duplication followed by divergence and specialization in function of different copies of the gene.

This article lists fourteen open problems in artificial life, each of which is a grand challenge requiring a major advance on a fundamental issue for its solution. Each problem is briefly explained, and, where deemed helpful, some promising paths to its solution are indicated.

Presents a descriptive natural history of the results of the evolution of differentiated multi-threaded (multi-cellular) self-replicating machine code programs (digital organisms), living in a network of computers, called the Tierra network. Programs are differentiated in that different threads execute different code (i.e. they express different genes). The seed organism develops into a mature ten-celled form, differentiated into a two-celled reproductive tissue and an eight-celled sensory tissue. The sensory threads obtain data about conditions on the machines in the network, and then process that data to choose the best machine to migrate to or to send the daughter to. Evolution leads to a diversity of algorithms for foraging for resources, primarily CPU time, on the network.

Efficiency is a problem in automatic programming, both in the programs produced and in the synthesis process itself. This paper presents a framework for using evolutionary mechanisms to guide program synthesis. A particular implementation of the framework, called Tierra, is described. Given a naive program and some limits on system resources, Tierra generates mutated programs and evolution proceeds by natural selection as the programs compete for Control Processing Unit (CPU) time and memory space. By applying the evolutionary mechanisms, Tierra has guided the automatic implementation of an efficient self-replicating program. The system is under continuing development; it is viewed more as a research tool than a prototype programming assistant; however, the performance of the system at present gives some hope for the ultimate feasibility of such systems.

Our concepts of biology, evolution, and complexity are constrained by having observed only a single instance of life, life on earth. A truly comparative biology is needed to extend these concepts. Because we cannot observe life on other planets, we are left with the alternative of creating Artificial Life forms on earth. I will discuss the approach of inoculating evolution by natural selection into the medium of the digital computer. This is not a physical/chemical medium; it is a logical/informational medium. Thus, these new instances of evolution are not subject to the same physical laws as organic evolution (e.g., the laws of thermodynamics) and exist in what amounts to another universe, governed by the “physical laws” of the logic of the computer. This exercise gives us a broader perspective on what evolution is and what it does. An evolutionary approach to synthetic biology consists of inoculating the process of evolution by natural selection into an artificial medium. Evolution is then allowed to find the natural forms of living organisms in the artificial medium. These are not models of life, but independent instances of life. This essay is intended to communicate a way of thinking about synthetic biology that leads to a particular approach: to understand and respect the natural form of the artificial medium, to facilitate the process of evolution in generating forms that are adapted to the medium, and to let evolution find forms and processes that naturally exploit the possibilities inherent in the medium. Examples are cited of synthetic biology embedded in the computational medium, where in addition to being an exercise in experimental comparative evolutionary biology, it is also a possible means of harnessing the evolutionary process for the production of complex computer software.