Performance of global stochastic optimisation methods under different constraints of kinetic models
by Egils Stalidzans (University of Latvia)
16:00 (40 min) in CT 7.01
Parameter estimation and optimisation tasks of kinetic models of metabolism usually are solved applying global stochastic optimisation methods. Their nice feature is universality and the drawback is that reaching optima is not guaranteed. How long should we run optimisation? Parallel optimisation runs are proposed for this problem introducing automatic detection of consensus and stagnation of a method. Performance of different optimisation methods is tested by parallel optimisation runs. Which one is the best?