Biomimetic evolutionary analysis: Robotically-simulated vertebrates in a predator-prey ecology

To test adaptation hypotheses about the evolution of animals, we need information about the behavior of phenotypically-variable individuals in a specific environment. To model behavior of ancient fish-like vertebrates, we previously combined evolutionary robotics and software simulations to create autonomous biomimetic swimmers in a simple aquatic environment competing and foraging for a single source of food. This system allowed us to test the hypothesis that selection for improved forage navigation drove the evolution of stiffer tails. In this paper, we extend our framework to evaluate more complex environments and hypotheses. Specifically, we test the hypothesis that predator-prey dynamics and the need for effective foraging strategies, operating simultaneously, were key selection pressures driving the evolution of morphological and sensory traits in early, fish-like vertebrates. Three evolvable traits were chosen because of their importance in propulsion and predator avoidance: (1) the number of vertebrae in the axial skeleton, (2) the trailing edge span of the caudal fin, and (3) the sensitivity of the sensory lateral line. To produce variable offspring, we used a genetic algorithm that rewarded parents with high fitness, allowing them to mate randomly and combine their mutated gametes. Offspring were then instantiated as autonomous embodied robots, the prey. These prey were chased by a non-evolving autonomous predator. Both kinds of robots were surface swimmers. The prey used a control architecture based on that of living fish: a two-layer subsumption architecture with predator escape over-riding steady swimming during foraging. The performance of six different prey robots in each generation was judged with a relative fitness function that rewarded a combination of high speed, rapid escape acceleration, escape responses, and the ability to stay away from the predator while at the same time staying close to the food source. This approach, which we call biomimet- ic evolutionary analysis, shows promise for investigators seeking new ways to test evolutionary hypotheses about biological systems.