One of the enduring puzzles in biology and the social sciences is the origin and persistence of altruism, whereby a behavior benefiting another individual incurs a direct cost for the individual performing the altruistic action. This apparent paradox was resolved by Hamilton’s theory, known as kin selection, which states that individuals can transmit copies of their own genes not only directly through their own reproduction but also indirectly by favoring the reproduction of kin, such as siblings or cousins. While many studies have provided qualitative support for kin selection theory, quantitative tests have not yet been possible due to the difficulty of quantifying the costs and benefits of helping acts. In this study, we conduct simulations with the help of a simulated system of foraging robots to manipulate the costs and benefits of altruism and determine the conditions under which altruism evolves.

By conducting experimental evolution over hundreds of generations of selection in populations with different costs and benefits of altruistic behavior, we show that kin selection theory always accurately predicts the minimum relatedness necessary for altruism to evolve. This high accuracy is remarkable given the presence of pleiotropic and epistatic effects, as well as mutations with strong effects on behavior and fitness. In addition to providing a quantitative test of kin selection theory in a system with a complex mapping between genotype and phenotype, this study reveals that a fundamental principle of natural selection also applies to synthetic organisms when these have heritable properties.

This project investigated the role of relatedness and levels of selection on evolution of cooperation and labour division in ant colonies. Experiments were carried out in simulation and on groups of real Alice micro-robots on four test cases, (1) heterogeneous colonies with individual level selection, (2) heterogeneous colonies with colony level selection, (3) homogeneous colonies with individual level selection and (4) homogeneous colonies with colony level selection.

During the first stage of experiments, we implemented and used a very fast probabilistic simulator to conduct an initial analysis of these four cases. In this simulation, decisions were not based on individual controllers, but instead on a probabilistic model. Probabilities were estimated in test runs with the real robotic setup. The advantage of such an approach is its simplicity and speed, allowing for a fast evaluation of the parameter ranges and yielding a very complete picture of the space of possible solutions…

In the second stage, we have finished the implementation of a physics-based, 2D simulator (available online here). Work on the simulator was conducted in cooperation with the ECAgents project. As opposed to the probabilistic simulator, decision making was based on individual controllers, which allowed a transfer of behavior evolved in simulation to the real Alice micro-robots. Direct evolution of the desired behaviors on real robots is far too time consuming for tasks with a high number of parameters. Benchmarks of our simulator at the start of this project showed that its simulation speed compared very favorably to commercially available alternatives less adapted to the specific needs of our project.