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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Reynolds, R. Dapeng Liu |
| Copyright Year | 2011 |
| Description | Author affiliation: Computer Science Department, Wayne State University, Detroit, MI (Reynolds, R.; Dapeng Liu) |
| Abstract | Within a cultural context we constantly deal effectively with multiple objectives. A computational version of cultural systems, Cultural Algorithms, has been extended to deal with multi-objective optimization problems. These approaches while employing the basic framework have used only a subset of the available knowledge sources. In this paper we present an extension of Cultural Algorithms for Multi-Objective optimization, MOCAT, the fully utilizes all of the available categories of knowledge sources. The synergy of this ensemble is demonstrated through the application to an example problem and the results compared with that of other approaches in metric terms. |
| Starting Page | 1233 |
| Ending Page | 1241 |
| File Size | 749497 |
| Page Count | 9 |
| File Format | |
| ISBN | 9781424478347 |
| e-ISBN | 9781424478354 |
| DOI | 10.1109/CEC.2011.5949757 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-06-05 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Cultural differences Optimization Heuristic algorithms Humans Fabrics Evolutionary computation Dynamic programming non-domination sort the cultural algorithm multi-objective evoluationary optimization Pareto front |
| Content Type | Text |
| Resource Type | Article |
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