Evolving Collective Behavior in an Artificial Ecology

Abstract

Collective behavior refers to coordinated group motion, common to many animals. The dynamics of a group can be seen as a distributed model, each "animal" applying the same rule set. This study investigates the use of evolved sensory controllers to produce schooling behavior. A set of artificial creatures "live" in an artificial world with hazards and food. Each creature has a simple artificial neural network brain that controls movement in different situations. A chromosome encodes the network structure and weights, which may be combined using artificial evolution with another chromosome, if a creature should choose to mate. Prey and predators coevolve without an explicit fitness function for schooling to produce sophisticated, nondeterministic, behavior. The work highlights the role of species' physiology in understanding behavior and the role of the environment in encouraging the development of sensory systems.

Species abundance patterns in an ecosystem simulation studied through Fisher’s logseries

“…A direct consequence of this choice is that our ecosystem encompasses individuals belonging to two trophic levels. Some other predator-prey models have already been proposed, such as the one in [65] for example. Yet, this particular agent model is dedicated to represent schooling behaviours, and the evolution is an offline mechanism using a genetic algorithm.…”

Self-Organized Coordinated Motion in Groups of Physically Connected Robots

“…As an example, it is worth mentioning the seminal work of Reynolds, who defines the behavior of virtual creatures, called "boids," making use of only local rules [32]. The work of Reynolds has stimulated many other studies on coordinated motion, which are all based on some biological inspiration [33], [34]. These works have self-organization as a common "feature" with the experiments presented in this paper.…”

Selection methods regulate evolution of cooperation in digital evolution

“…While all five selection methods are frequently used to simulate differential selection (PSM in [19][20][21][22][23][24][25][26][27][28][29][30][31][32]; RSM in [33,34]; TPSM in [35][36][37]; TUSM in [38][39][40][41][42][43][44][45][46], TSM in [22,47,48]), the choice between them is rarely justified. Moreover, little attempt has been made to quantify the effects of selection methods on the dynamics of the digital evolution (but see [22,49]).…”

“…To address this problem, we investigate theoretically and with numerical experiments how the five selection methods regulate the evolution of cooperation. We focus on cooperation, because digital evolution is especially popular in this domain [19][20][21][22][23][24][26][27][28][29]33,38,41,47,48,54,55], and it is an important biological phenomenon that has attracted extensive scientific interest (see [56][57][58][59][60] for reviews). We consider a population of related individuals, each having a genotype that consists of a haploid allele encoding for cooperation or defection.…”