Abstract
Genetic Algorithms (GAs) are a commonly used stochastic search
heuristic which have been applied to a plethora of problem domains.
GAs work on a population of chromosomes (an encoding of a solution
to the problem at hand) and breed solutions from fit parents to
hopefully produce fitter children through a process of crossover and
mutation. This work discusses two novel crossover approaches for GAs
when applied to the optimisation of time-series problems, with particular
application to bio-control schedules.
heuristic which have been applied to a plethora of problem domains.
GAs work on a population of chromosomes (an encoding of a solution
to the problem at hand) and breed solutions from fit parents to
hopefully produce fitter children through a process of crossover and
mutation. This work discusses two novel crossover approaches for GAs
when applied to the optimisation of time-series problems, with particular
application to bio-control schedules.
| Original language | English |
|---|---|
| Title of host publication | SLS-DS 2007 Doctoral Symposium on Engineering Stochastic Local Search Algorithms |
| Editors | Enda Ridge, Thomas Stützle, Mauro Birattari, Holger H. Hoos |
| Place of Publication | Belgium |
| Publisher | IRIDIA |
| Pages | 47-51 |
| Number of pages | 5 |
| Publication status | Published - 2007 |
Publication series
| Name | IRIDIA – Technical Report Series |
|---|---|
| Publisher | IRIDIA |
| ISSN (Print) | 1781-3794 |
Keywords
- genetic algorithims
- chromosomes
- crossover approaches
- bio-control schedules