EFFECTS OF ADAPTIVE SOCIAL NETWORKS ON THE ROBUSTNESS OF EVOLUTIONARY ALGORITHMS

Abstract

Biological networks are structurally adaptive and take on non-random topological properties that influence system robustness. Studies are only beginning to reveal how these structural features emerge, however the influence of component fitness and community cohesion (modularity) have attracted interest from the scientific community. In this study, we apply these concepts to an evolutionary algorithm and allow its population to self-organize using information that the population receives as it moves over a fitness landscape. More precisely, we employ fitness and clustering based topological operators for guiding network structural dynamics, which in turn are guided by population changes taking place over evolutionary time. To investigate the effect on evolution, experiments are conducted on six engineering design problems and six artificial test functions and compared against cellular genetic algorithms and panmictic evolutionary algorithm designs. Our results suggest that a self-organizing topology evolutionary algorithm can exhibit robust search behavior with strong performance observed over short and long time scales. More generally, the coevolution between a population and its topology may constitute a promising new paradigm for designing adaptive search heuristics.