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dc.contributor.advisorCairns, David Edward-
dc.contributor.advisorSmith, Leslie S.-
dc.contributor.authorGodley, Paul Michael-
dc.identifier.citationMaximising the Efficiency of Bio-Control Application Utilising Genetic Algorithms at the 5th Biennial Conference of the European Federation of IT in Agriculture (EFITA) 2007en
dc.identifier.citationNovel Genetic Algorithm Crossover Approaches for Time-Series Problems at the Engineering Stochastic Local Search Algorithms (SLS 2007) Doctoral Symposiumen
dc.identifier.citationDirected Intervention Crossover applied to Bio-Control Scheduling at the IEEE Congress of Evolutionary Computation (CEC) 2007en
dc.identifier.citationOptimisation of Cancer Chemotherapy Schedules Using Directed at the IEEE Congress of Evolutionary Computation (CEC) 2008en
dc.identifier.citationBiocontrol in Mushroom Farming Using a Markov Network EDA at the IEEE Congress of Evolutionary Computation (CEC) 2008en
dc.identifier.citationOptimisation and Fitness Modelling of Bio-control in Mushroom Farming using a Markov Network EDA at GECCO 08en
dc.identifier.citationThe Effects of Mutation and Directed Intervention Crossover when applied to Scheduling Chemotherapy at GECCO 08en
dc.identifier.citationFitness Directed Intervention Crossover Approaches applied to Bio-Scheduling Problems at IEEE CIBCB 2008en
dc.identifier.citationDirected Intervention Crossover Approaches Applied to the Optimisation of Schedules at the WSSEC2008 Workshop and Summer School on Evolutionary Compuing Lecture Series by Pioneersen
dc.description.abstractGenetic Algorithms (GAs) are a search heuristic modeled on the processes of evolution. They have been used to solve optimisation problems in a wide variety of fields. When applied to the optimisation of intervention schedules for optimal control problems, such as cancer chemotherapy treatment scheduling, GAs have been shown to require more fitness function evaluations than other search heuristics to find fit solutions. This thesis presents extensions to the GA crossover process, termed directed intervention crossover techniques, that greatly reduce the number of fitness function evaluations required to find fit solutions, thus increasing the effectiveness of GAs for problems of this type. The directed intervention crossover techniques use intervention scheduling information from parent solutions to direct the offspring produced in the GA crossover process towards more promising areas of a search space. By counting the number of interventions present in parents and adjusting the number of interventions for offspring schedules around it, this allows for highly fit solutions to be found in less fitness function evaluations. The validity of these novel approaches are illustrated through comparison with conventional GA crossover approaches for optimisation of intervention schedules of bio-control application in mushroom farming and cancer chemotherapy treatment. These involve optimally scheduling the application of a bio-control agent to combat pests in mushroom farming and optimising the timing and dosage strength of cancer chemotherapy treatments to maximise their effectiveness. This work demonstrates that significant advantages are gained in terms of both fitness function evaluations required and fitness scores found using the proposed approaches when compared with traditional GA crossover approaches for the production of optimal control schedules.en
dc.publisherUniversity of Stirlingen
dc.subjectGenetic Algorithmsen
dc.subjectOptimal Controlen
dc.subjectDirected Intervention Crossoveren
dc.subject.lcshGenetics Computer simulationen
dc.subject.lcshGenetics Mathematical modelsen
dc.subject.lcshControl theoryen
dc.titleDirected Intervention Crossover Approaches in Genetic Algorithms with Application to Optimal Control Problemsen
dc.typeThesis or Dissertationen
dc.type.qualificationnameDoctor of Philosophyen
dc.contributor.affiliationSchool of Natural Sciences-
dc.contributor.affiliationComputing Science and Mathematics-
Appears in Collections:Computing Science and Mathematics eTheses

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