In contrast to single-objective optimisation problems, in which diversity had to be discovered as a usable resource, diversity is inherently present in multi-objective optimisation problems. The presence of diversity is most obvious when considering a Pareto front, in which solutions to multi-objective optimisation problems must involve trade-offs to satisfy the conflicting demands of different objective functions. Several algorithms with different prop-erties and varying levels of complexity have been proposed for finding Pareto optimal fronts. The main class of algorithms used for this task are the non-dominated sorting genetic algorithms, NSGA-II and NSGA-III.