Optimizing the stochastic fleet estimation model using evolutionary computation
| Autor: | Zack Zhu, S. Wesolkowski |
|---|---|
| Rok vydání: | 2008 |
| Předmět: |
Set (abstract data type)
Mathematical optimization ComputingMethodologies_SIMULATIONANDMODELING Computer science Stochastic process Computer Science::Neural and Evolutionary Computation Monte Carlo method Genetic algorithm Interactive evolutionary computation Genetic representation Function (mathematics) Evolutionary computation |
| Zdroj: | 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence). |
| Popis: | We introduce an evolutionary computation framework using genetic algorithms to optimize the Stochastic Fleet Estimation (SaFE) model. SaFE ias a Monte Carlo-based model which generates a vehicle fleet based on the set of requirements that the fleet is supposed to accomplish. a genetic algorithm framework is used in order to alternate solutions between different plausible sets of platforms. We use SaFE coupled with a simple cost evaluation based on the output of SaFE as the genetic algorithm's cost function. Results showing a decrease in fleet cost are shown and analyzed. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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