@article{2006003,
abstract = {The present-day evolutionary multi-objective optimization (EMO) algorithms had a demonstrated history of evolution over the years. The initial EMO methodologies involved additional niching parameters which made them somewhat subjective to the user. Fortunately, soon enough parameter-less EMO methodologies have been suggested thereby making the earlier EMO algorithms unpopular and obsolete. In this paper, we present a functional decomposition of a viable EMO methodology and discuss the critical components which require special attention for making the complete algorithm free from any additional parameter. A critical evaluation of existing EMO methodologies suggest that the elitist non-dominated sorting GA (NSGA-II) is one of EMO algorithms which does not require any additional implicit or explicit parameters other than the standard EA parameters, such as population size, operator probabilities, etc. This parameter-less property of NSGA-II is probably the reason for its popularity to most EMO studies thus far. {\textcopyright} Springer-Verlag Berlin Heidelberg 2007.},
author = {Deb, Kalyanmoy},
doi = {10.1007/978-3-540-69432-8_12},
isbn = {3540694315},
issn = {1860949X},
journal = {Studies in Computational Intelligence},
pages = {241--257},
publisher = {Springer, Berlin, Heidelberg},
title = {{Evolutionary multi-objective optimization without additional parameters}},
url = {https://link.springer.com/chapter/10.1007/978-3-540-69432-8{\_}12},
volume = {54},
year = {2007}
}