Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Designing Evolutionary Algorithms for Dynamic Environments
Designing Evolutionary Algorithms for Dynamic Environments
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
Particle filters for state estimation of jump Markov linear systems
IEEE Transactions on Signal Processing
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
Brief paper: Random sampling approach to state estimation in switching environments
Automatica (Journal of IFAC)
Monte Carlo techniques for prediction and filtering of non-linear stochastic processes
Automatica (Journal of IFAC)
Stochastic volatility modeling with computational intelligence particle filters
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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In this paper we propose hybrid metaheuristic particle filters for the dual estimation of state and parameters in a stochastic volatility estimation problem. We use evolutionary strategies and real coded genetic algorithms as the metaheuristics. The hybrid metaheuristic particle filters provide accurate results while using lesser number of particles for this high dimension estimation problem. We compare the performance of our hybrid algorithms with a sequential importance resampling particle filter (SIR) and the parameter learning algorithm (PLA). Our hybrid particle filters out perform both these algorithms for this particular dual estimation problem.