Abstract
Our work to date has reviewed novel directed intervention crossover approaches as an effective way of guiding the crossover process. These techniques have been successfully applied to the scheduling of both bio-control agents for mushroom farming and chemotherapy drugs for cancer treatment and in both instances outperform Uniform Crossover (UC). Unlike traditional UC, the directed intervention approaches actively choose an intervention level and spread based on the ¿tness of the parents selected for crossover. This paper reports on the results derived from this study thus far.
| Original language | English |
|---|---|
| Title of host publication | 2008 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology |
| Publisher | IEEE |
| Pages | 120-127 |
| Number of pages | 8 |
| ISBN (Print) | 9781424417780 |
| DOIs | |
| Publication status | Published - 2008 |
Keywords
- Crossover
- Optimal Control
- Scheduling
- Evolutionary Algorithms
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Cite this
Godley, P. M., Cairns, D. E., Cowie, J., & McCall, J. (2008). Fitness directed intervention crossover approaches applied to bio-scheduling problems. In 2008 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (pp. 120-127). IEEE. https://doi.org/10.1109/CIBCB.2008.4675768