@inproceedings{b3135afe7f8e45ef834bb204bfc0eb01,
title = "Incrementing multi-objective evolutionary algorithms: Performance studies and comparisons",
abstract = "This paper addresses the issue by presenting a novel {"}incrementing{"} multi-objective evolutionary algorithm (IMOEA) with dynamic population size that is adaptively computed according to the on-line discovered trade-off surface and its desired population distribution density. It incorporates the method of fuzzy boundary local perturbation with interactive local fine-tuning for broader neighborhood exploration to achieve better convergence as well as discovering any gaps or missing trade-off regions at each generation. Comparative studies with other multi-objective (MO) optimization are performed on benchmark problem. The new suggested quantitative measures together with other well-known measures are employed to access and compare their performances statistically.",
author = "Tan, \{K. C.\} and Lee, \{T. H.\} and Khor, \{E. F.\}",
year = "2001",
month = mar,
doi = "10.1007/3-540-44719-9\_8",
language = "English",
isbn = "978-3-540-41745-3",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Berlin Heidelberg",
pages = "111--125",
editor = "Eckart Zitzler and Thiele, \{Lothar \} and Deb, \{Kalyanmoy \} and Coello, \{Carlos Artemio Coello\} and David Corne",
booktitle = "Evolutionary Multi-Criterion Optimization",
note = "1st International Conference on Evolutionary Multi-Criterion Optimization, EMO 2001 ; Conference date: 07-03-2001 Through 09-03-2001",
}