TABLE 2.

Nature‐inspired evolutionary ecosystems

Name/focus	Description	Results/findings	References
Bugs world	A two‐dimensional lattice with adaptive bugs and food whose distribution changes over time by diffusion.	Early example of intrinsic adaptation is where evolution is driven by many interacting subsystems, without an explicit fitness function.	(Packard, 1989)
Tierra	An ecosystem of evolving computer programs that are competing for processing time and memory space.	An exploration of fundamental processes of evolutionary and ecological dynamics such as punctuated equilibrium, host–parasite co‐evolution, and density‐dependent natural selection.	(Ray, 1991)
Evolutionary reinforcement learning (ERL)	A model where adaptive agents travel across a two‐dimensional lattice at random, encountering food, predators, hiding places, and other items.	Learning and evolution together are more successful in producing adaptive populations compared to either alone.	(Ackley & Littman, 1991)
Avida	A Tierra‐inspired (Ray, 1991) evolutionary biology software platform to conduct experiments with self‐replicating and evolving computer programs.	Spatial geometry helps the development of diversity and adaptive capabilities.	(Adami & Brown, 1994)
PARE	Parasitoid Artificial Ecosystem, a parasitoid/host model based on parasitic wasps and their insect hosts in natural systems.	Large population sizes can be achieved in artificial models where the dynamics mimic natural systems and are derived through the behaviors of independent components.	(Olson & Sequeira, 1995)
Swarm	An agent‐based simulation of a collection of agents performing a string of actions.	Swarm supports hierarchical modeling approaches.	(Minar et al., 1996)
Sugarscape	One of the earliest artificial worlds consists of agents, rules, environment, and sugar, the only food source for agent survival.	Has been used to investigate social dynamics including marital status, inheritance, and evolution.	(Epstein & Axtell, 1996)
PlantWorld	A 2D grid world upon which stationary agents, i.e., plants, germinate from seeds, maintain themselves, grow, reproduce, and die.	A demonstration of feedback between population attributes and evolutionary dynamics dependent on environmental perturbations.	(Dyer & Bentley, 2002)
Evolve IV	An evolutionary ecosystem model that is individual‐based and metabolically driven.	The model captures the reciprocal interaction between biota and their surroundings, for example, the initial population pollutes the environment to its own detriment.	(Brewster et al., 2002)
Organism Interaction	An artificial life model where organisms fight for space and resources but can also form mutualistic relationships.	When organisms gather a certain amount of nutrition, reproduction and mutation occur, and mutation creates new varieties of organisms.	(Pachepsky et al., 2002)
Mosaic World	An evolutionary ecosystem where “critters” evolve visual receptors and behaviors in a visually ambiguous world.	Explores computational principles by which color vision can emerge, overcoming ambiguity and usefully guiding behavior.	(Schlessinger et al., 2005)
Masbiole	A multi‐agent simulation that uses symbiotic learning and evolution where agents can learn or evolve depending on their symbiotic relations toward other agents.	Masbiole agents can escape Nash equilibria and can be used to solve general computational problems such as optimization.	(Eguchi et al., 2006)
Singing robots	A group of autonomous robots that interact and imitate each other using vocal‐like sounds to create music.	The robots learn to imitate each other by babbling heard intonation patterns to evolve vectors of motor control parameters to synthesize the imitations.	(Miranda, 2008)
EcoSim	An individual‐based predator–prey model where each agent behavior is characterized by a fuzzy cognitive map, allowing the agent's behavior to evolve as the simulation runs.	The simulation shows coherent behaviors with the emergence of strong correlation patterns also observed in existing ecosystems.	(Gras et al., 2009)
EcoDemics	A model of epidemic spread in EcoSim (Gras et al., 2009).	The dynamics of the ecosystem, along with the spatial distribution of agents, play a significant role in disease progression.	(Majdabadi Farahani, 2014)
Evolutionary geometric agent‐based model	Incorporating evolutionary algorithms in Geometric Framework combined with agent‐based models to explore nutritional strategies of agents in the presence of competition.	Competition combined with reproductive skew in social groups can play a role in the evolution of diet breadth.	(Senior et al., 2015)