metric
Modification of the crowding distance to avoid infinite values for the first and last point in each domain.
Crowding distance computation see Deb, K., Agrawal, S., Pratap, A.
Crowding distance computation see Deb, K., Agrawal, S., Pratap, A. & Meyarivan, T. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. Lecture notes in computer science 1917, 849–858 (2000).
Hypervolume computation based on variant 3 of the algorithm in the paper: C.
Hypervolume computation based on variant 3 of the algorithm in the paper: C. M. Fonseca, L. Paquete, and M. Lopez-Ibanez. An improved dimension-sweep algorithm for the hypervolume indicator. In IEEE Congress on Evolutionary Computation, pages 1157-1163, Vancouver, Canada, July 2006.
FIXE: The implementation is ugly, as the algorithm as directly been translated from python
Distance to the K Nearest Neighbours using the KD-Tree algorithm
Modification of the crowding distance to avoid infinite values for the first and last point in each domain. The crowding for the second and last element of each domain is instead the distance between the first and second (or last and second but last).
Crowding distance computation see Deb, K., Agrawal, S., Pratap, A. & Meyarivan, T. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. Lecture notes in computer science 1917, 849–858 (2000).