@article{Deb2003c,
abstract = {In this paper, we suggest a distributed computing approach for finding multiple Pareto-optimal solutions. When the number of objective functions is large, the resulting Pareto-optimal front is of large dimension, thereby requiring a single processor multi-objective EA (MOEA) to use a large population size and run for a large number of generations. However, the task of finding a well-distributed set of solutions on the Pareto-optimal front can be distributed among a number of processors, each pre-destined to find a particular portion of the Pareto-optimal set. Based on the guided domination approach [1], here we propose a modified domination criterion for handling problems with a convex Pareto-optimal front. The proof-of-principle results obtained with a parallel version of NSGA-II shows the efficacy of the proposed approach. {\textcopyright} Springer-Verlag Berlin Heidelberg 2003.},
author = {Deb, Kalyanmoy and Zope, Pawan and Jain, Abhishek},
doi = {10.1007/3-540-36970-8_38},
isbn = {3540018697},
issn = {16113349},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
pages = {534--549},
publisher = {Springer, Berlin, Heidelberg},
title = {{Distributed computing of Pareto-optimal solutions with evolutionary algorithms}},
url = {https://link.springer.com/chapter/10.1007/3-540-36970-8{\_}38},
volume = {2632},
year = {2003}
}