Fitness directed intervention crossover approaches applied to bio-scheduling problems

Paul M. Godley; David E. Cairns; Julie Cowie; John McCall

doi:10.1109/CIBCB.2008.4675768

Fitness directed intervention crossover approaches applied to bio-scheduling problems

Paul M. Godley, David E. Cairns, Julie Cowie, John McCall

Yunus Centre

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Citations (Scopus)

Abstract

This paper discusses the effects of using directed intervention crossover approaches with Genetic Algorithms (GA) and demonstrates their application to scheduling of bio-control agents and cancer chemotherapy treatments. Unlike traditional approaches such as Single Point Crossover (SPC) or Uniform Crossover (UC), the directed intervention techniques actively choose the intervention level based on the fitness of the parents selected for crossover. This work shows that a fitness directed intervention crossover approach leads to significant improvements over SPC and UC when applied to the two different scheduling problems.

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
Event	2008 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology - Sun Valley Resort, Sun Valley Idaho, United States Duration: 15 Sept 2008 → 17 Sept 2008

Conference

Conference	2008 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology
Abbreviated title	CIBCB 2008
Country/Territory	United States
City	Sun Valley Idaho
Period	15/09/08 → 17/09/08

Keywords

Crossover
Optimal Control
Scheduling
Evolutionary Algorithms

ASJC Scopus subject areas

Artificial Intelligence
Computational Theory and Mathematics
Biomedical Engineering
Health Informatics

UN SDGs

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

Documents and Links

10.1109/CIBCB.2008.4675768

Cite this

@inproceedings{14774b7c09c0476ab94b631fa29917e2,

title = "Fitness directed intervention crossover approaches applied to bio-scheduling problems",

abstract = "This paper discusses the effects of using directed intervention crossover approaches with Genetic Algorithms (GA) and demonstrates their application to scheduling of bio-control agents and cancer chemotherapy treatments. Unlike traditional approaches such as Single Point Crossover (SPC) or Uniform Crossover (UC), the directed intervention techniques actively choose the intervention level based on the fitness of the parents selected for crossover. This work shows that a fitness directed intervention crossover approach leads to significant improvements over SPC and UC when applied to the two different scheduling problems.",

keywords = "Crossover, Optimal Control, Scheduling, Evolutionary Algorithms",

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

year = "2008",

doi = "10.1109/CIBCB.2008.4675768",

language = "English",

isbn = "9781424417780",

pages = "120--127",

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

publisher = "IEEE",

address = "United States",

note = "2008 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2008 ; Conference date: 15-09-2008 Through 17-09-2008",

}

Godley, PM, Cairns, DE, 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., 4675768, IEEE, pp. 120-127, 2008 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, Sun Valley Idaho, United States, 15/09/08. https://doi.org/10.1109/CIBCB.2008.4675768

TY - GEN

T1 - Fitness directed intervention crossover approaches applied to bio-scheduling problems

AU - Godley, Paul M.

AU - Cairns, David E.

AU - Cowie, Julie

AU - McCall, John

PY - 2008

Y1 - 2008

N2 - This paper discusses the effects of using directed intervention crossover approaches with Genetic Algorithms (GA) and demonstrates their application to scheduling of bio-control agents and cancer chemotherapy treatments. Unlike traditional approaches such as Single Point Crossover (SPC) or Uniform Crossover (UC), the directed intervention techniques actively choose the intervention level based on the fitness of the parents selected for crossover. This work shows that a fitness directed intervention crossover approach leads to significant improvements over SPC and UC when applied to the two different scheduling problems.

AB - This paper discusses the effects of using directed intervention crossover approaches with Genetic Algorithms (GA) and demonstrates their application to scheduling of bio-control agents and cancer chemotherapy treatments. Unlike traditional approaches such as Single Point Crossover (SPC) or Uniform Crossover (UC), the directed intervention techniques actively choose the intervention level based on the fitness of the parents selected for crossover. This work shows that a fitness directed intervention crossover approach leads to significant improvements over SPC and UC when applied to the two different scheduling problems.

KW - Crossover

KW - Optimal Control

KW - Scheduling

KW - Evolutionary Algorithms

U2 - 10.1109/CIBCB.2008.4675768

DO - 10.1109/CIBCB.2008.4675768

M3 - Conference contribution

SN - 9781424417780

SP - 120

EP - 127

BT - 2008 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

PB - IEEE

T2 - 2008 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

Y2 - 15 September 2008 through 17 September 2008

ER -

Fitness directed intervention crossover approaches applied to bio-scheduling problems

Abstract

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Documents and Links

Fingerprint

Cite this