Ecological theory provides insights about evolutionary computation
Emily L. Dolson, Charles Ofria · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2018
Promoting diversity in an evolving population is important for Evolutionary Computation (EC) because it reduces premature convergence on suboptimal fitness peaks while still encouraging both exploration and exploitation [3]. However, some types of diversity facilitate finding global optima better than other types. For example, a high mutation rate may maintain high population-level diversity, but all of those genotypes are clustered in a local region of a fitness landscape. Fitness sharing [3], on the other hand, promotes diversity via negative density dependence forcing solutions apart. Lexicase selection [5] goes one step further, dynamically selecting for diverse phenotypic traits, encouraging solutions to actively represent many portions of the landscape.