The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
An updated survey of GA-based multiobjective optimization techniques
Open accessAbstract
After using evolutionary techniques for single-objective optimization during more than two decades, the incorporation of more than one objective in the fitness function has finally become a popular area of research. As a consequence, many new evolutionary-based approaches and variations of existing techniques have recently been published in the technical literature. The purpose of this paper is to summarize and organize the information on these current approaches, emphasizing the importance of analyzing the operations research techniques in which most of them are based, in an attempt to motivate researchers to look into these mathematical programming approaches for new ways of exploiting the search capabilities of evolutionary algorithms. Furthermore, a summary of the main algorithms behind these approaches is provided, together with a brief criticism that includes their advantages and disadvantages, degree of applicability, and some known applications. Finally, further trends in this area and some possible paths for further research are also addressed.
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Cited by
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Zhang, Q., & Li, H. (2007) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2007.892759 · Cited by 10,069
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A survey and annotated bibliography of multiobjective combinatorial optimization
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MOGOA algorithm for constrained and unconstrained multi-objective optimization problems
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Metadata
- Authors
- Coello, C. A.
- Venue
- ACM Computing Surveys
- Published
- 2000-06
- Volume
- 32
- Issue
- 2
- Pages
- 109-143
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2036109172
- Cited by
- 773
- References
- 49
- FWCI
- 20.70
- Type
- journal article
- Language
- en
- Open access
- bronze
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