An Updated Survey of Evolutionary Multiobjective Optimization Techniques: State of the Art and Future Trends (1999)
| Venue: | Proceedings of the Congress on Evolutionary Computation |
| Citations: | 61 - 0 self |
BibTeX
@INPROCEEDINGS{Coello99anupdated,
author = {Carlos A. Coello Coello},
title = {An Updated Survey of Evolutionary Multiobjective Optimization Techniques: State of the Art and Future Trends},
booktitle = {Proceedings of the Congress on Evolutionary Computation},
year = {1999},
pages = {3--13},
publisher = {IEEE Press}
}
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Abstract
This paper reviews some of the most popular evolutionary multiobjective optimization techniques currently reported in the literature, indicating some of their main applications, their advantages, disadvantages, and degree of aplicability. Finally, some of the most promising areas of future research are briefly discussed. 1 Introduction Since the pioneering work of Rosenberg in the late 1960s regarding the possibility of using genetic-based search to deal with multiple objectives [1], this new area of research (now called Evolutionary Multi-Objective Optimization, or EMOO for short) has grown considerably as indicates the notable increment (mainly in the last 15 years) of technical papers in international conferences and peer-reviewed journals, special sessions in international conferences and interest groups in the Internet 1 . Multiobjective optimization is with no doubt a very important research topic both for scientists and engineers, not only because of the multiobjective nature...
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