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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Haiming Lu Yen, G.G. |
| Copyright Year | 2002 |
| Description | Author affiliation: Sch. of Electr. & Comput. Eng., Oklahoma State Univ., Stillwater, OK, USA (Haiming Lu; Yen, G.G.) |
| Abstract | The authors propose a new evolutionary approach to multiobjective optimization problems; the Dynamic Multiobjective Evolutionary Algorithm (DMOEA). In DMOEA, a population growing and population decline strategies are designed, and several important indicators are defined in order to determine the adaptive individual "killing" scheme. By examining the selected performance indicators of a test function, DMOEA is found to be effective in directing the population into an optimal population size, keeping the diversity of the individuals along the trade-off surface, tending to extend the Pareto front to new areas, and finding a well-approximated Pareto optimal front. |
| Sponsorship | IEEE IEEE Neural Networks Soc. IEE Evolutionary Programming Soc |
| Starting Page | 1648 |
| Ending Page | 1653 |
| File Size | 667853 |
| Page Count | 6 |
| File Format | |
| ISBN | 0780372824 |
| DOI | 10.1109/CEC.2002.1004489 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2002-05-12 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Evolutionary computation Distributed computing Convergence Testing Stochastic processes Sampling methods Optimization methods Computational complexity Perturbation methods Heuristic algorithms |
| Content Type | Text |
| Resource Type | Article |
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