Enhancing Quality-Diversity Optimization Through Domain-Specific Dissimilarity as Crowding Distance

Quality-diversity algorithms aim to simultaneously optimize solution performance and maintain diversity within a population. In this paper, we explore the use of NSGA-II as a quality-diversity algorithm for the evolutionary design of 3D structures, modifying its crowding distance calculation to utilize dissimilarity measures. While NSGA-II is widely employed for multi-objective optimization, its use of fitness for calculating crowding distance may not be the most effective for tasks requiring solution diversity. We propose leveraging both genetic and phenotypic dissimilarity metrics to improve diversity management. To evaluate this approach, we compare the standard NSGA-II using fitness-based crowding distance and Diversity-Enhancing NSGA-II (DE-NSGA-II) using various combinations of dissimilarity-based metrics for crowding distance and diversity scores. Experiments are conducted using two distinct genetic representations on two optimization tasks: height of the center of gravity of passive structures and velocity of active structures. Results demonstrate the potential of dissimilarity-based crowding distance to enhance the diversity and overall quality of solutions in complex evolutionary design tasks.