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Compared to other variants of the Kalman filter (KF), EnKF is straightforward to implement, as it employs random ensembles to represent solution states.
It does not require the binary encoding to represent solution like genetic algorithm and not employ a probability density function to self-adapt its individuals like evolution strategy.
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One branch corresponds to the classic boundary layer solution for active lid plate tectonics while two new branches represent solutions for sluggish lid convection.
ADCQGA makes use of double-individuals to represent solutions that are classified as feasible and infeasible solutions.
We represent solutions to the heat-equation by the expectation of an appropriate stochastic functional on path space.
For solving MKPs, binary strings are used to represent solutions and two repair operators are applied to guarantee the feasibility of the solutions.
First, a new encoding scheme is presented to represent solutions reasonably, and a new decoding scheme is presented to map solutions to feasible schedules.
Matrices are used to represent solutions.
I believe both these companies represent solutions for future needs that apply for a very large target audience.
This allows one to represent solutions of Eq. (5) via the following convolution-based formula.
It is not difficult to show that formulas (24) and (36) really represent solutions to system (1).
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