In this work we investigate how various techniques of diversity control employed during evolution of 3D agents influence the velocity they achieve, and how these techniques influence the diversity of behaviors across multiple independent evolutionary runs. Three evolutionary settings are compared: a standard generational evolutionary process where fitness is velocity, a niching technique, and pure novelty search. Two genetic encodings (lower and higher level) and two environments (land and water) are used in experiments. To diversify behaviors, seven properties of movement introduced earlier are calculated for each individual during evolution. Best individuals obtained from evolution in each setting are compared both in terms of their fitness and the similarity of their movement patterns.

Although determining the similarity of genotypes is often employed in artificial life experiments to measure or control diversity, in practical applications we may often be more interested in similarities of phenotypes. The latter may provide information about the effective diversity in a population, and thus it may be more suitable for diversity estimations and diversity-based search algorithms. A phenotype of a simulated creature can be understood as creature’s physiology or its behavior – e.g., body kinematics, movement patterns, or gaits. In this paper, we introduce a set of efficient measures which allow for describing the movement of simulated 3D stick creatures. We use these measures to analyze the results of evolutionary optimization of virtual creatures towards four unique behavioral goals. We show that most solutions obtained for each goal occupy distinct areas of the phenotype space. This suggests that measures defined in this paper create a useful behavioral space for movement-related fitness functions. Finally, we use the introduced measures to visualize how the properties of movement change in populations during the course of evolution.