Sentence examples for as the algorithm evolves from inspiring English sources
This paper suggests using a method of evaluation which changes as the algorithm evolves: whereas accuracy is initially compromised to improve the speed of the algorithm, the process is subsequently altered to produce a more accurate, evaluation process.
It can be seen that as iteration time increases, both mean and variance in (30) increase linearly with the time index, that is, as the algorithm evolves.
The right picture in Figure 3 shows the evolution of classical modularity Q for the Internet graph as a function of the resolution t, i.e. as the algorithm evolves (from right to left).
Furthermore, under certain smoothness assumptions for the target (i.e., that π(x) is twice continuously differentiable), it also guarantees that this expectation tends to zero as the number of iterations increases, hence implying that less points are added as the algorithm evolves.Finally, note that Theorem 3 has been derived only for η t (z,d)=η t (d).
In brief, using the free energy of RNA secondary structures for a given input sequence as the fitness function, the algorithm evolves a population of RNA conformers by exchanging parts of the conformers, inserting mutations in structural elements and selecting the best fit individual conformations resulting from these operations.
The algorithm evolves through an iterative cycle, in which the search space is explored.
The MDP Procedure as an algorithm evolves in the allocation space and stops when the Samuelson's Condition is met so that the public good quantity is optimal, and simultaneously the private good is allocated in a Pareto optimal way, i.e., (z^=(x^,y_{1}^,ldots,y_{n}^)) is Pareto optimal.
The MDP Procedure as an algorithm evolves in the allocation space and stops when the Samuelson's conditions are met so that the public good quantity is optimal, and simultaneously the private good is allocated in a Pareto optimal way, i.e., ( x, y 1, …, y N ) is Pareto optimal.
Consequently, taking the expectation on both sides of (14), under A.1-A.3, the mean weight-error vector of the proposed algorithm evolves as E [ v n + 1 ] = E [ v n ] + μ α ̄ n E e n x n ∥ x n ∥ 2 + ( 1 - α ̄ n ) E e n 3 x n ∥ x n ∥ 2. (15).
The proposed algorithm evolves a pool of solutions using principles of scatter search, interlinked with an iterated local search as an improvement method.
The dynamic priorities introduced by the WFO algorithm evolve as a function of the specific channel conditions and currently experienced QoS of each service flow in a cross-layer higher layers/MAC/PHY approach.
