Optimised Crossover Genetic Algorithms for Combinatorial Optimisation Problems
A Genetic Algorithm is successful in generating near -optimal solutions if it is able to produce o®spring during crossover that is better than the parent solutions. Most of the current methods of crossover determine o®spring by using a stochastic approach and without reference to the objective func...
Gorde:
| Egile nagusia: | Nazif, Habibeh |
|---|---|
| Formatua: | Thesis |
| Hizkuntza: | English |
| Argitaratua: |
2010
|
| Sarrera elektronikoa: | http://ethesis.upm.edu.my/id/eprint/6001/1/FS_2010_53.pdf |
| Etiketak: |
Etiketa erantsi
Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!
|
Antzeko izenburuak
-
Optimised Crossover Genetic Algorithms for Combinatorial Optimisation Problems
nork: Nazif, Habibeh
Argitaratua: (2010) -
Optimised crossover genetic algorithms for combinatorial optimisation problems /
nork: Nazif, Habibeh. -
Genetic algorithms with optimised crossover operator
nork: Nazif, Habibeh, et al.
Argitaratua: (2009) -
Optimised crossover genetic algorithm for capacitated vehicle routing problem
nork: Nazif, Habibeh, et al.
Argitaratua: (2012) -
Fuzzy genetic algorithms for combinatorial optimisation problems
nork: Varnamkhasti, Mohammad Jalali
Argitaratua: (2012)
