| Autor: |
Goldberg, David E., Koza, John R., Coello, Carlos A. Coello, Lamont, Gary B., Veldhuizen, David A. Van |
| Zdroj: |
Evolutionary Algorithms for Solving Multi-Objective Problems; 2007, p1-60, 60p |
| Abstrakt: |
Problems with multiple objectives arise in a natural fashion in most disciplines and their solution has been a challenge to researchers for a long time. Despite the considerable variety of techniques developed in Operations Research (OR) and other disciplines to tackle these problems, the complexities of their solution calls for alternative approaches. The use of evolutionary algorithms (EAs) to solve problems of this nature has been motivated mainly because of the population-based nature of EAs which allows the generation of several elements of the Pareto optimal set in a single run. Additionally, the complexity of some multiobjective optimization problems (MOPs) (e.g., very large search spaces, uncertainty, noise, disjoint Pareto curves, etc.) may prevent use (or application) of traditional OR MOPsolution techniques. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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