Genetic 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
