In this paper, we consider a new particle filter inspired
by biological evolution. In the standard particle filter, a resampling
scheme is used to decrease the degeneracy phenomenon and improve
estimation performance. Unfortunately, however, it could cause the
undesired the particle deprivation problem, as well. In order to
overcome this problem of the particle filter, we propose a novel
filtering method called the genetic filter. In the proposed filter, we
embed the genetic algorithm into the particle filter and overcome the
problems of the standard particle filter. The validity of the proposed
method is demonstrated by computer simulation.
 Kalman, R.E, "A New Approach to Linear Filtering and Prediction
Problems," Trans, ASME, J. Basic Engineering, vol.82, pp34-45, Mar.
 B. Ristic, S. Arulampalam, and N. Gordon, Beyond the Kalman Filter:
Particle Filters for Tracking Applications, Boston, London: Artech
 S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics, Cambridge,
Massachisetts, London, England: The MIT Press, 2005.
 J.E. Handschin and D.Q. Mayne, "Monte Carlo techniques to estimate the
conditional expectation in multi-stage non-linear filtering," Intern.
Journal of Control, vol.9, no. 5, pp.547-559, 1969.
 K. Uosaki, Y. Kimura and T. Hatanaka, "Nonlinear State Estimation by
Evolution Strategies based Particle Filters," Proc. Congress on
Evolutionary Computation Vol. 1, pp884 - 890, 19-23-th June 2004.
 Z. Michalewicz. Genetic Algorithm + Data Structure = Evolution
Programs. Berling Heidelberg, New York: Springer-Verlag, third ed.,