Division of labor is a hallmark strategy employed by a wide variety of groups ranging in complexity from bacteria to human economies. Within these groups, some individuals, such as worker ants, sacrifice their ability to reproduce and instead dedicate their lives to the maintenance of the colony and success of their kin. A worker ant may spend its entire life performing a single task, such as defending the colony or tending to the brood. The complexity of the strategies employed by these groups, combined with their rampant success, gives rise to questions regarding why division of labor exists. While extensive research has been done to better understand the patterns and mechanisms of division of labor, exploring this topic in an evolutionary context remains challenging to study due to the slow pace of evolution and imperfect historical data.
Understanding how and why division of labor arises is pertinent not just for understanding biological phenomena, but also as a means to enable evolutionary computation techniques to address complex problems using problem decomposition. The objective of problem-decomposition approaches is to have a group of individuals cooperatively solve a complex task by breaking it into pieces, having specialist individuals solve the pieces, and reassembling the solution. Essentially, problem-decomposition approaches use division of labor to enable groups to solve more challenging problems than any individual could alone. Unfortunately, human engineers have struggled with creating effective, automated problem-decomposition approaches.
In this dissertation, I use digital evolution (i.e., populations of self-replicating computer programs that undergo open-ended evolution) to investigate questions related to the evolution of division of labor and to apply these insights to problem decomposition techniques. This dissertation has three primary components: First, we provide experimental evidence that evolutionary computation techniques can evolve groups of individuals that exhibit division of labor. Second, we explore two hypotheses for the evolution of division of labor. Specifically, we find support for the hypothesis that temporal polyethism (i.e., where a worker’s age is related to the task it performs within the colony) may result from the evolutionary pressures of aging and risks associated with tasks. Additionally, we find support for a hypothesis initially proposed by Adam Smith, the premier economist, that the presence of task-switching costs results in an increase in the amount of division of labor exhibited by groups. Third, we describe how our analyses revealed that groups of organisms evolved as part of our task-switching work exhibit complex problem decomposition strategies that can potentially be applied to other evolutionary computation challenges. This work both informs biological studies of division of labor and also provides insights that can enable the development of new mechanisms for using evolutionary computation to solve increasingly complex engineering problems.