Zamin, an Agent Based Artificial Life Model

We review the applications of artificial life (ALife), the creation of synthetic life on computers to study, simulate, and understand living systems. The definition and features of ALife are shown by application studies. ALife application fields treated include robot control, robot manufacturing, practical robots, computer graphics, natural phenomenon modeling, entertainment, games, music, economics, Internet, information processing, industrial design, simulation software, electronics, security, data mining, and telecommunications. In order to show the status of ALife application research, this review primarily features a survey of about 180 ALife application articles rather than a selected representation of a few articles. Evolutionary computation is the most popular method for designing such applications, but recently swarm intelligence, artificial immune network, and agent-based modeling have also produced results. Applications were initially restricted to the robotics and computer graphics, but presently, many different applications in engineering areas are of interest.

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The Simulated Evolution of Biochemical Guilds: Reconciling Gaia Theory and Natural Selection

Artificial Life ◽

10.1162/106454699568791 ◽

1999 ◽

Vol 5 (4) ◽

pp. 291-318 ◽

Cited By ~ 34

Author(s):

Keith Downing ◽

Peter Zvirinsky

Keyword(s):

Natural Selection ◽

Artificial Life ◽

Large Scale ◽

Resource Competition ◽

Nutrient Recycling ◽

Nutrient Ratios ◽

Evolutionary Mechanisms ◽

Life Model ◽

Gaia Theory ◽

Implicit Coordination

Gaia theory, which states that organisms both affect and regulate their environment, poses an interesting problem to Neo-Darwinian evolutionary biologists and provides an exciting set of phenomena for artificial-life investigation. The key challenge is to explain the emergence of biotic communities that are capable, via their implicit coordination, of regulating large-scale biogeochemical factors such as the temperature and chemical composition of the biosphere, but to assume no evolutionary mechanisms beyond contemporary natural selection. Along with providing an introduction to Gaia theory, this article presents simulations of Gaian emergence based on an artificial-life model involving genetic algorithms and guilds of simple metabolizing agents. In these simulations, resource competition leads to guild diversity; the ensemble of guilds then manifests life-sustaining nutrient recycling and exerts distributed control over environmental nutrient ratios. These results illustrate that standard individual-based natural selection is sufficient to explain Gaian self-organization, and they help clarify the relationships between two key metrics of Gaian activity: recycling and regulation.

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