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A digital organism is a self-replicating computer program that mutates and evolves. Digital organisms are used as a tool to study the dynamics of Darwinian evolution, and to test or verify specific hypotheses or mathematical models of evolution. The study of digital organisms is closely related to the area of artificial life.
Digital organisms can be traced back to the game Darwin, developed in 1961 at Bell Labs, in which computer programs had to compete with each other by trying to stop others from executing . [1] A similar implementation that followed this was the game Core War. In Core War, it turned out that one of the winning strategies was to replicate as fast as possible, which deprived the opponent of all computational resources. Programs in the Core War game were also able to mutate themselves and each other by overwriting instructions in the simulated "memory" in which the game took place. This allowed competing programs to embed damaging instructions in each other that caused errors (terminating the process that read it), "enslaved processes" (making an enemy program work for you), or even change strategies mid-game and heal themselves.
Steen Rasmussen at Los Alamos National Laboratory took the idea from Core War one step further in his core world system by introducing a genetic algorithm that automatically wrote programs. However, Rasmussen did not observe the evolution of complex and stable programs. It turned out that the programming language in which core world programs were written was very brittle, and more often than not mutations would completely destroy the functionality of a program.
The first to solve the issue of program brittleness was Thomas S. Ray with his Tierra system, which was similar to core world. Ray made some key changes to the programming language such that mutations were much less likely to destroy a program. With these modifications, he observed for the first time computer programs that did indeed evolve in a meaningful and complex way.
Later, Chris Adami, Titus Brown, and Charles Ofria started developing their Avida system, [2] which was inspired by Tierra but again had some crucial differences. In Tierra, all programs lived in the same address space and could potentially execute or otherwise interfere with each other's code. In Avida, on the other hand, each program lives in its own address space. Because of this modification, experiments with Avida became much cleaner and easier to interpret than those with Tierra. With Avida, digital organism research has begun to be accepted as a valid contribution to evolutionary biology by a growing number of evolutionary biologists. Evolutionary biologist Richard Lenski of Michigan State University has used Avida extensively in his work. Lenski, Adami, and their colleagues have published in journals such as Nature [3] and the Proceedings of the National Academy of Sciences (USA). [4]
In 1996, Andy Pargellis created a Tierra-like system called Amoeba that evolved self-replication from a randomly seeded initial condition. More recently REvoSim - a software package based around binary digital organisms - has allowed evolutionary simulations of large populations that can be run for geological timescales. [5]
Evolution is the change in the heritable characteristics of biological populations over successive generations. It occurs when evolutionary processes such as natural selection and genetic drift act on genetic variation, resulting in certain characteristics becoming more or less common within a population over successive generations. The process of evolution has given rise to biodiversity at every level of biological organisation.
In computer science, evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character.
Tierra is a computer simulation developed by ecologist Thomas S. Ray in the early 1990s in which computer programs compete for time and space. In this context, the computer programs in Tierra are considered to be evolvable and can mutate, self-replicate and recombine. Tierra's virtual machine is written in C. It operates on a custom instruction set designed to facilitate code changes and reordering, including features such as jump to template.
Neuroevolution, or neuro-evolution, is a form of artificial intelligence that uses evolutionary algorithms to generate artificial neural networks (ANN), parameters, and rules. It is most commonly applied in artificial life, general game playing and evolutionary robotics. The main benefit is that neuroevolution can be applied more widely than supervised learning algorithms, which require a syllabus of correct input-output pairs. In contrast, neuroevolution requires only a measure of a network's performance at a task. For example, the outcome of a game can be easily measured without providing labeled examples of desired strategies. Neuroevolution is commonly used as part of the reinforcement learning paradigm, and it can be contrasted with conventional deep learning techniques that use backpropagation with a fixed topology.
Avida is an artificial life software platform to study the evolutionary biology of self-replicating and evolving computer programs. Avida is under active development by Charles Ofria's Digital Evolution Lab at Michigan State University; the first version of Avida was designed in 1993 by Ofria, Chris Adami and C. Titus Brown at Caltech, and has been fully reengineered by Ofria on multiple occasions since then. The software was originally inspired by the Tierra system.
Experimental evolution is the use of laboratory experiments or controlled field manipulations to explore evolutionary dynamics. Evolution may be observed in the laboratory as individuals/populations adapt to new environmental conditions by natural selection.
In population genetics and population ecology, population size is a countable quantity representing the number of individual organisms in a population. Population size is directly associated with amount of genetic drift, and is the underlying cause of effects like population bottlenecks and the founder effect. Genetic drift is the major source of decrease of genetic diversity within populations which drives fixation and can potentially lead to speciation events.
Thomas S. Ray is an evolutionary biologist known for his research in tropical biology, digital evolution, and the human mind.
Richard E. Lenski is an American evolutionary biologist, the John A. Hannah Distinguished Professor of Microbial Ecology at Michigan State University. He is a member of the National Academy of Sciences and a MacArthur Fellow. Lenski is best known for his still ongoing 36-year-old long-term E. coli evolution experiment, which has been instrumental in understanding the core processes of evolution, including mutation rates, clonal interference, antibiotic resistance, the evolution of novel traits, and speciation. He is also well known for his pioneering work in studying evolution digitally using self-replicating organisms called Avida.
The theory of facilitated variation demonstrates how seemingly complex biological systems can arise through a limited number of regulatory genetic changes, through the differential re-use of pre-existing developmental components. The theory was presented in 2005 by Marc W. Kirschner and John C. Gerhart.
Christoph Carl Herbert "Chris" Adami is a professor of microbiology and molecular genetics, as well as professor of physics and astronomy, at Michigan State University. He is a core faculty member of the Ecology, Evolution, and Behavior (EEB) Program there.
Langton's loops are a particular "species" of artificial life in a cellular automaton created in 1984 by Christopher Langton. They consist of a loop of cells containing genetic information, which flows continuously around the loop and out along an "arm", which will become the daughter loop. The "genes" instruct it to make three left turns, completing the loop, which then disconnects from its parent.
Autoconstructive evolution is a process in which the entities undergoing evolutionary change are themselves responsible for the construction of their own offspring and thus for aspects of the evolutionary process itself. Because biological evolution is always autoconstructive, this term mainly occurs in evolutionary computation, to distinguish artificial life type systems from conventional genetic algorithms where the GA performs replication artificially. The term was coined by Lee Spector.
Dr. Charles A. Ofria is a Professor in the Department of Computer Science and Engineering at Michigan State University, the director of the Digital Evolution (DEvo) Lab there, and Director of the BEACON Center for the Study of Evolution in Action. He is the son of the late Charles Ofria, who developed the first fully integrated shop management program for the automotive repair industry. Ofria attended Stuyvesant High School and graduated from Ward Melville High School in 1991. He obtained a B.S. in Computer Science, Pure Mathematics, and Applied Mathematics from Stony Brook University in 1994, and a Ph.D. in Computation and Neural Systems from the California Institute of Technology in 1999. Ofria's research focuses on the interplay between computer science and Darwinian evolution.
The evolution of biological complexity is one important outcome of the process of evolution. Evolution has produced some remarkably complex organisms – although the actual level of complexity is very hard to define or measure accurately in biology, with properties such as gene content, the number of cell types or morphology all proposed as possible metrics.
The E. coli long-term evolution experiment (LTEE) is an ongoing study in experimental evolution begun by Richard Lenski at the University of California, Irvine, carried on by Lenski and colleagues at Michigan State University, and currently overseen by Jeffrey Barrick at the University of Texas at Austin. It has been tracking genetic changes in 12 initially identical populations of asexual Escherichia coli bacteria since 24 February 1988. Lenski performed the 10,000th transfer of the experiment on March 13, 2017. The populations reached over 73,000 generations in early 2020, shortly before being frozen because of the COVID-19 pandemic. In September 2020, the LTEE experiment was resumed using the frozen stocks. When the populations reached 75,000 generations, the LTEE was transferred from the Lenski lab to the Barrick lab. In August 2024, the LTEE populations passed 80,000 generations in the Barrick lab.
Evolutionary biology, in particular the understanding of how organisms evolve through natural selection, is an area of science with many practical applications. Creationists often claim that the theory of evolution lacks any practical applications; however, this claim has been refuted by scientists.
In evolutionary biology, robustness of a biological system is the persistence of a certain characteristic or trait in a system under perturbations or conditions of uncertainty. Robustness in development is known as canalization. According to the kind of perturbation involved, robustness can be classified as mutational, environmental, recombinational, or behavioral robustness etc. Robustness is achieved through the combination of many genetic and molecular mechanisms and can evolve by either direct or indirect selection. Several model systems have been developed to experimentally study robustness and its evolutionary consequences.
Evolving digital ecological networks are webs of interacting, self-replicating, and evolving computer programs that experience the same major ecological interactions as biological organisms. Despite being computational, these programs evolve quickly in an open-ended way, and starting from only one or two ancestral organisms, the formation of ecological networks can be observed in real-time by tracking interactions between the constantly evolving organism phenotypes. These phenotypes may be defined by combinations of logical computations that digital organisms perform and by expressed behaviors that have evolved. The types and outcomes of interactions between phenotypes are determined by task overlap for logic-defined phenotypes and by responses to encounters in the case of behavioral phenotypes. Biologists use these evolving networks to study active and fundamental topics within evolutionary ecology.
The Extended Evolutionary Synthesis (EES) consists of a set of theoretical concepts argued to be more comprehensive than the earlier modern synthesis of evolutionary biology that took place between 1918 and 1942. The extended evolutionary synthesis was called for in the 1950s by C. H. Waddington, argued for on the basis of punctuated equilibrium by Stephen Jay Gould and Niles Eldredge in the 1980s, and was reconceptualized in 2007 by Massimo Pigliucci and Gerd B. Müller.
