This makes the algorithm particularly suited to parallel computations.
This favourable property, however, makes the algorithm prohibitively slow.
This halting behavior makes the algorithm even more effective at searching large intervals.
This characteristic makes the algorithm suitable for large scale synthesis problems.
This makes the algorithm a valuable alternative to the time-consuming traditional approaches available to geologists.
An adaptive mutation probability in the PIRGA makes the algorithm have more chance to jump out of local optima.
The method employs hardware friendly binary correlation matrix memories (CMM), which makes the algorithm itself hardware friendly.
This approach makes the algorithm analogous to the approach of the classical Gauss-Newton method for nonlinear regression, and we call our method the GN algorithm.
Additionally, the use of size-dependent scatter matrices to compute the activation of the neurons makes the algorithm more adaptable to different data distributions.
This flexibility not only enables fast and efficient implementation on current CPUs and accelerator architectures like GPUs or Intel MIC, but it also makes the algorithm future-proof.
In particular, we obtain convergence to optimum fitness by resorting to an annealing process, which makes the algorithm asymptotically equivalent to simulated annealing.
