Mathematical statistics (4th ed.)
Mathematical statistics (4th ed.)
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Fitting algebraic curves to noisy data
Journal of Computer and System Sciences - STOC 2002
An effective use of crowding distance in multiobjective particle swarm optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Fault diagnosis using dynamic trend analysis: A review and recent developments
Engineering Applications of Artificial Intelligence
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An important aspect of stochastic simulation is the development of realistic input scenarios. This work describes a technique for determining the frequencies of transitions between input prototypes by fitting historic data. Instead of deciding on a single objective function, multiple curves are fit that are Pareto optimal in terms of a number of objectives using the Multi-objective Particle Swarm Optimisation algorithm. The objectives are: fit error, number of curves and curvature of the prototypes. For this study, prototypes were chosen that represent first order step responses. The fit prototypes are then interpreted as being a certain type of event. The resulting list of possible event sequences is used to populate an event transition probability matrix with better coverage than any one fit would have given.