Abstract
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.
Keywords
Computer scienceEvolutionary algorithmMulti-objective optimizationData scienceManagement scienceArtificial intelligenceMachine learning
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Publication Info
- Year
- 2000
- Type
- review
- Volume
- 32
- Issue
- 2
- Pages
- 109-143
- Citations
- 753
- Access
- Closed
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Cite This
Carlos A. Coello Coello
(2000).
An updated survey of GA-based multiobjective optimization techniques.
ACM Computing Surveys
, 32
(2)
, 109-143.
https://doi.org/10.1145/358923.358929
Identifiers
- DOI
- 10.1145/358923.358929