@inproceedings{Deb2005,
abstract = {With the advent of efficient techniques for multi-objective evolutionary optimization (EMO), real-world search and optimization problems are being increasingly solved for multiple conflicting objectives. During the past decade, most emphasis has been spent on finding the complete Pareto-optimal set, although EMO researchers were always aware of the importance of procedures which would help choose one particular solution from the Pareto-optimal set for implementation. This is also one of the main issues on which the classical and EMO philosophies are divided on. In this paper, we address this long-standing issue and suggest an interactive EMO procedure which, for the first time, will involve a decision-maker in the evolutionary optimization process and help choose a single solution at the end. This study is the culmination of many year's of research on EMO and would hopefully encourage both practitioners and researchers to pay more attention in viewing the multi-objective optimization as an aggregate task of optimization and decision-making. {\textcopyright} Springer-Verlag Berlin Heidelberg 2005.},
author = {Deb, Kalyanmoy and Chaudhuri, Shamik},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/11590316_111},
isbn = {3540305068},
issn = {03029743},
pages = {690--695},
publisher = {Springer, Berlin, Heidelberg},
title = {{I-EMO: An interactive evolutionary multi-objective optimization tool}},
url = {https://link.springer.com/chapter/10.1007/11590316{\_}111},
volume = {3776 LNCS},
year = {2005}
}