Fitness directed intervention crossover approaches applied to bio-scheduling problems

Paul Michael Godley; David E. Cairns; Julie Cowie; James McCall

doi:10.1109/CIBCB.2008.4675768

Fitness directed intervention crossover approaches applied to bio-scheduling problems

Paul Michael Godley, David E. Cairns, Julie Cowie, James McCall

NMAHP

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

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	https://doi.org/10.1109/CIBCB.2008.4675768
Publication status	Published - 2008

Keywords

Crossover
Optimal Control
Scheduling
Evolutionary Algorithms

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Documents and Links

10.1109/CIBCB.2008.4675768

Cite this

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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.",

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author = "Godley, {Paul Michael} and Cairns, {David E.} and Julie Cowie and James McCall",

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booktitle = "2008 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology",

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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) › peer-review

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 -

Fitness directed intervention crossover approaches applied to bio-scheduling problems

Abstract

Keywords

UN SDGs

Documents and Links

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