Abstract
| 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 |
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Keywords
- Crossover
- Optimal Control
- Scheduling
- Evolutionary Algorithms
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Fitness directed intervention crossover approaches applied to bio-scheduling problems. / Godley, Paul Michael; Cairns, David E.; Cowie, Julie; McCall, James.
2008 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. IEEE, 2008. p. 120-127.Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed)
TY - CHAP
T1 - Fitness directed intervention crossover approaches applied to bio-scheduling problems
AU - Godley, Paul Michael
AU - Cairns, David E.
AU - Cowie, Julie
AU - McCall, James
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
KW - Crossover
KW - Optimal Control
KW - Scheduling
KW - Evolutionary Algorithms
U2 - 10.1109/CIBCB.2008.4675768
DO - 10.1109/CIBCB.2008.4675768
M3 - Chapter (peer-reviewed)
SN - 9781424417780
SP - 120
EP - 127
BT - 2008 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology
PB - IEEE
ER -