Dr David Cairns & Mr Geoffrey Neumann – University of Stirling
Introducing Intervention Targeting into Estimation of Distribution Algorithms
Targetted EDA (TEDA) is a new hybrid Genetic Algorithm (GA) crossover
approach that combines a targeted intervention principle with Estimation
of Distribution Algorithms (EDA) to solve optimal control problems. In
problems such as our sample problem, scheduling chemotherapy treatment the
number of interventions used is an important part of solution fitness.
Fitness Directed Crossover (FDC) is a modified GA crossover method that
actively chooses the number of interventions to set in new solutions based
on the number in fit existing solutions. EDA are able to find fit
solutions by discovering patterns within a set of fit solutions. TEDA uses
FDC to select a suitable number of interventions to set while using an EDA
based approach to select which interventions to set. Results suggest that
by combining the two approaches, TEDA is able to outperform both EDA and
FDC on a sample optimal control problem.
School of Computing, Robert Gordon University, St Andrew Street, Aberdeen, Lecture Room C48, 14:15 – 15:15.