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Journal Articles
Publisher: Journals Gateway
Artificial Life (2020) 26 (2): 274–306.
Published: 01 May 2020
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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.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2006) 12 (4): 617–634.
Published: 01 October 2006
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Cancer can be viewed as the loss of cooperative cell behaviors that normally facilitate multicellularity, including the formation of tissues and organs. Hanahan and Weinberg describe the phenotypic differences between healthy and cancerous cells in an article titled “The Hallmarks of Cancer” ( Cell, 100, 57–70, 2000). Here the authors propose six phenotypic changes at the cellular level as the essential hallmarks of cancer. They investigate the dynamics and interactions of these hallmarks in a model known as CancerSim. They describe how CancerSim implements the hallmarks in an agent-based simulation which can help test the hypotheses put forth by Hanahan and Weinberg. Experiments with CancerSim are described that study the interactions of cell phenotype alterations, and in particular, the likely sequences of precancerous mutations, known as pathways . The experiments show that sequencing is an important factor in tumorigenesis, as some mutations have preconditions—they are selectively advantageous only in combination with other mutations. CancerSim enables a modeler to study the dynamics of a developing tumor and simulate how progression can be altered by tuning model parameters.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2000) 6 (4): 325–345.
Published: 01 October 2000
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The transformation of normal cells into cancerous cells is an evolutionary process. Populations of precancerous cells reproduce, mutate, and compete for resources. Some of these mutations eventually lead to cancer. We calculate the probability of developing cancer under a set of simplifying assumptions and then elaborate these calculations, culminating in a simple simulation of the cell dynamics. The agent-based model allows us to examine the interactions of mutations critical for the development of cancer that are either evolutionarily neutral or selective. We can also examine the interaction of these mutations with a “mutator phenotype” derived from mutations that raise the mutation rate for the entire cell. The simulations suggest that there must be at least two selectively neutral mutations necessary for the development of cancer and that preventive treatments will be most effective when they increase this number. The model also suggests that selective mutations facilitate the development of cancer, so that the more selective mutations necessary for the development of cancer, the greater the chance of developing it.
Journal Articles
Publisher: Journals Gateway
Artificial Life (1997) 3 (3): 165–190.
Published: 01 July 1997
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Echo is a generic ecosystem model in which evolving agents are situated in a resource-limited environment. The Echo model is described, and the behavior of Echo is evaluated on two well-studied measures of ecological diversity: relative species abundance and the species-area scaling relation. In simulation experiments, these measures are used to compare the behavior of Echo with that of a neutral model, in which selection on agent genotypes is random. These simulations show that the evolutionary component of Echo makes a significant contribution to its behavior and that Echo shows good qualitative agreement with naturally occurring species abundance distributions and species-area scaling relations.
Journal Articles
Publisher: Journals Gateway
Artificial Life (1994) 1 (3): 267–289.
Published: 01 April 1994
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Genetic algorithms are computational models of evolution that play a central role in many artificial-life models. We review the history and current scope of research on genetic algorithms in artificial life, giving illustrative examples in which the genetic algorithm is used to study how learning and evolution interact, and to model ecosystems, immune system, cognitive systems, and social systems. We also outline a number of open questions and future directions for genetic algorithms in artificial-life research.