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EnglishCompact GAs (cGAs) are search methods that, instead of evolving a population of solutions, process a probability vector describing the distribution of a hypothetical population with update rules inspired to the typical selection and recombination operations of a GA. The cGAs well lend themselves to real-time implementations in constrained, low capacity microcontrollers, as they have reduced memory requirement and better distributed computational loads with respect to the standard, population-based GA. This paper investigates the applicability of two cGAs selected from literature to optimize online a PI controller for an induction motor drive. The experimental results are particularly promising, and suggest interesting directions for further research
| Autori: | |
| Titolo: | Elitist compact genetic algorithms for induction motor self-tuning control |
| Data di pubblicazione: | 2006 |
| Nome del convegno: | IEEE Congress on Evolutionary Computation, CEC 2006 |
| ISBN: | 978-0-7803-9487-2 |
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1109/CEC.2006.1688695 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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