The objective of the NSGA algorithm is to improve the adaptive fit of a population of candidate solutions to a Pareto front constrained by a set of objective functions. The algorithm uses an evolutionary process with surrogates for evolutionary operators including selection, genetic crossover, and genetic mutation. The population is sorted into a hierarchy of sub-populations based on the ordering of Pareto dominance. Similarity between members of each sub-group is evaluated on the Pareto front, and the resulting groups and similarity measures are used to promote a diverse front of non-dominated solutions.
The SortByRankAndDistance function orders the population into a hierarchy of non-dominated Pareto fronts. The CrowdingDistance-Assignment calculates the average distance between members of each front on the front itself. The Crossover-AndMutation function performs the classical crossover and mutation genetic operators of the Genetic Algorithm. Both the SelectParentsBy-RankAndDistance and SortByRankAndDistance functions discriminate members of the population first by rank (order of dominated precedence of the front to which the solution belongs) and then distance within the front (calculated by CrowdingDistanceAssignment).
NSGA was designed for and is suited to continuous function multiple objective optimization problem instances. A binary representation can be used in conjunction with classical genetic operators such as one-point crossover and point mutation. A real-valued representation is recommended for continuous function optimization problems, in turn requiring representation specific genetic operators such as Simulated Binary Crossover (SBX) and polynomial mutation