TY - JOUR
AU1 - Fortin, Félix-Antoine
AU2 - Parizeau, Marc
AB -  Revisiting the NSGA-II Crowding-Distance Computation Félix-Antoine Fortin Marc Parizeau felix-antoine.fortin.1@ulaval.ca marc.parizeau@gel.ulaval.ca Laboratoire de vision et systèmes numériques Département de génie électrique et de génie informatique Université Laval, Québec (Québec), Canada G1V 0A6 ABSTRACT This paper improves upon the reference NSGA-II procedure by removing an instability in its crowding distance operator. This instability stems from the cases where two or more individuals on a Pareto front share identical fitnesses. In those cases, the instability causes their crowding distance to either become null, or to depend on the individual's position within the Pareto front sequence. Experiments conducted on nine different benchmark problems show that, by computing the crowding distance on unique fitnesses instead of individuals, both the convergence and diversity of NSGA-II can be significantly improved. Categories and Subject Descriptors I.2.8 [Artificial Intelligence]: Problem Solving, Control Methods, and Search--heuristic methods Keywords Multi-objective evolutionary algorithms; NSGA-II; crowding distance 1. INTRODUCTION The last decade was a fertile breeding ground for multiobjective evolutionary algorithms (MOEAs) [1]. They have led to several well-known second generation algorithms such as SPEA2 [16], PAES [10], PESA [2] and NSGA-II [5], that have emerged over time as reference platforms for solving real world multi-objective problems. But Zhou et 
TI - Revisiting the NSGA-II crowding-distance computation
DA - 2013-07-06
UR - https://www.deepdyve.com/lp/association-for-computing-machinery/revisiting-the-nsga-ii-crowding-distance-computation-gq1qmythmo
DP - DeepDyve
ER -