Genetic Team Composition and Level of Selection in the Evolution of Cooperation

Abstract

Abstract-In cooperative multiagent systems, agents interact to solve tasks. Global dynamics of multiagent teams result from local agent interactions, and are complex and difficult to predict. Evolutionary computation has proven a promising approach to the design of such teams. The majority of current studies use teams composed of agents with identical control rules ("genetically homogeneous teams") and select behavior at the team level ("team-level selection"). Here we extend current approaches to include four combinations of genetic team composition and level of selection. We compare the performance of genetically homogeneous teams evolved with individual-level selection, genetically homogeneous teams evolved with team-level selection, genetically heterogeneous teams evolved with individual-level selection, and genetically heterogeneous teams evolved with team-level selection. We use a simulated foraging task to show that the optimal combination depends on the amount of cooperation required by the task. Accordingly, we distinguish between three types of cooperative tasks and suggest guidelines for the optimal choice of genetic team composition and level of selection.

HyperNEAT for Locomotion Control in Modular Robots

“…Although extending the scope of their findings to this scenario might be tenuous, Waibel et al have shown that for tasks requiring co-operation, homogeneous teams outperform heterogeneous ones [12]. The modular differentiation approach allows us to enjoy the best of both worlds: it exploits the benefit that homogeneous teams enjoy without sacrificing the advantages of specialisation.…”

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.…”

The Evolution of Division of Labor

“…Numerous evolutionary computation approaches have been used to study the behavior of cooperative groups comprising heterogeneous members [5][6][7][8][9]. Two key differentiating characteristics for these approaches are the level of selection used (i.e., individual or group) and whether or not division of labor…”

“…Cooperation among these species occurs only at the time of fitness evaluation, when individuals from one species are evaluated with representatives from each of the other species. PerezUribe et al [7] and Waibel et al [8] overview work performed in this area, and also describe the effects of varying the levels of selection and population composition (i.e., heterogeneous or homogeneous populations) [8]. However, these prior studies do not address multi-level selection, where organisms experience individual-level competition to survive and also group-level pressure to cooperate.…”