A key challenge in evolutionary robotics is the computational cost of evolutionary runs. The high computational cost forces researchers to rely on power-hungry computer clusters and, even with these, researchers often are faced with long evaluation cycles that make development of evolutionary experiments a time consuming and tedious effort. In this paper we address this challenge on two fronts. We have developed an evolutionary robotic engine where all individuals are evaluated in parallel using a thread-based implementation on a graphical processing unit (GPU). This engine allows us to run an evolutionary robotics experiment in seconds on a modest laptop. The second avenue of exploration is that we have used this engine to study the role of initial robot poses in fitness evaluation. We find that if we co-evolve initial pose and controller competitively, we can reduce the evaluation period of individuals significantly. Combined the evolutionary robotics engine and the co-evolutionary approach are significant demonstrations of how to make evolutionary robotics more computationally efficient.

In evolutionary robotics, we evaluate individuals by placing them in an initial configuration in the environment, and then measure their fitness over a period of time. The choice of initial configuration has a direct impact on the fitness of an individual and thereby also the overarching evolutionary process. In this paper, we propose the concept of dynamic initial configurations, which is an initial configuration that is neither random nor fixed, but develops dynamically in response to the evolutionary process. As an example we have implemented a competitive co-evolutionary algorithm where initial configurations and controllers are evolved together to solve an obstacle avoidance task of a mobile robot. We show that, while a evolutionary approach taken from literature consistently fails, the co-evolutionary approach succeeds in 22 out of 25 runs. This example demonstrates the benefit of dynamic initial configurations, but more work is needed to establish if the concept generalizes to more complex tasks, environments and morphologies.

An ongoing discussion in biology concerns whether intrinsic mortality, or senescence, is programmed or not. The death (i.e. removal) of an individual solution is an inherent feature in evolutionary algorithms that can potentially explain how intrinsic mortality can be beneficial in natural systems. This paper investigates the relationship between mutation rate and mortality rate with a steady state genetic algorithm that has a specific intrinsic mortality rate. Experiments were performed on a predefined deceptive fitness landscape, the hierarchical if-and-only-if function (H-IFF). To test whether the relationship between mutation and mortality rate holds for more complex systems, an agent-based spatial grid model based on the H-IFF function was also investigated. This paper shows that there is a direct correlation between the evolvability of a population and an indiscriminate intrinsic mortality rate to mutation rate ratio. Increased intrinsic mortality or increased mutation rate can cause a random drift that can allow a population to find a global optimum. Thus, mortality in evolutionary algorithms does not only explain evolvability, but might also improve existing algorithms for deceptive/rugged landscapes. Since an intrinsic mortality rate increases the evolvability of our spatial model, we bolster the claim that intrinsic mortality can be beneficial for the evolvability of a population.

Due to the replacement of natural flora and fauna with urban environments, a significant part of the earths organisms that function as primary consumers have been dispelled. To compensate for the reduction in the amount of primary consumers, robotic systems that mimic plant-like organisms are interesting to mimic for their potential functional and aesthetic value in urban environments. To investigate how to utilize plant developmental strategies in order to engender urban artificial plants, we built a simple evolutionary model that applies an L-System based grammar as an abstraction of plant development. In the presented experiments, phytomorphologies (plant morphologies) are iteratively constructed using a context sensitive L-System. The genomic representation of the L-System is subject to mutation by an evolutionary algorithm. These mutations thus alter the developmental rules of these phytomorphologies. We compare the differences between the light absorption of evolving virtual plants that remain static during their life and virtual plants that possess the possibility to move joints that link the separate parts of the virtual plants. Our results show that our evolutionary algorithm did not exploit potential beneficial joint actuation, instead, mostly static structures evolved. The results of our evolving L-System show that it is able to create various phytomorphologies, albeit that the results are preliminary and will be more thoroughly investigated in the future.