The main real-coded genetic algorithm (RCGA) research effort has been spent on developing efficient crossover operators. This study presents a taxonomy for this operator that groups its instances in different categories according to the way they generate the genes of the offspring from the genes of
Robust confidence intervals applied to crossover operator for real-coded genetic algorithms
✍ Scribed by Domingo Ortiz-Boyer; César Hervás-Martínez; Nicolás García-Pedrajas
- Publisher
- Springer
- Year
- 2007
- Tongue
- English
- Weight
- 685 KB
- Volume
- 12
- Category
- Article
- ISSN
- 1432-7643
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Most real-coded genetic algorithm research has focused on developing effective crossover operators, and as a result, many different types of crossover operators have been proposed. Some forms of crossover operators are more suitable to tackle certain problems than others, even at the different stage
## Abstract The primary objective of this study is to propose a real‐coded hypercubic distributed genetic algorithm (HDGA) for optimizing reservoir operation system. A conventional genetic algorithm (GA) is often trapped into local optimums during the optimization procedure. To prevent premature co