Fitness directed intervention crossover approaches applied to bio-scheduling problems

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

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publication2008 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology
PublisherIEEE
Pages120-127
Number of pages8
ISBN (Print)9781424417780
DOIs
Publication statusPublished - 2008

Keywords

  • Crossover
  • Optimal Control
  • Scheduling
  • Evolutionary Algorithms

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Biomedical Engineering
  • Health Informatics

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