Numerical values for the estimation of the Pareto front in the hill-climbing algorithm.</p
<p>The tradeoff between energy cost and RMS tracking error shown as a Pareto front.</p
<p>Numerical values of the skin friction coefficient for different values of physical parameters.</...
<p>Numerical values of skin friction coefficients for different values of physical parameters.</p
Numerical values for the estimation of the Pareto front in the genetic algorithm.</p
The line represents the non-dominated solutions found with the multi-objective hill climbing algorit...
Numerical values for all graphs in the main figures and supporting information.</p
The obtained cost function values with respect to each run of different algorithms.</p
<p>Parameters of the different approaches algorithms depending on the computational budget.</p
<p>Illustration of Pareto fronts obtained by the complete enumeration with different values of .</p
<p>The numerical results of averaged PEP produced by all the three methods for each data-truncation ...
<p>The numerical value of the most ideal point (a), the least ideal point (b) and the mathematical e...
Number of fields on the Pareto front for each quality and random effect combination.</p
<p>Parameters derived from fitting of data to the Hill Equation with a Hill co-efficient of one.</p
In all subfigures red lines are contours of noise performance function, blue lines are contours of f...
The values of each parameter for each step of running proposed algorithm on the DAG graph of Fig 3.<...
<p>The tradeoff between energy cost and RMS tracking error shown as a Pareto front.</p
<p>Numerical values of the skin friction coefficient for different values of physical parameters.</...
<p>Numerical values of skin friction coefficients for different values of physical parameters.</p
Numerical values for the estimation of the Pareto front in the genetic algorithm.</p
The line represents the non-dominated solutions found with the multi-objective hill climbing algorit...
Numerical values for all graphs in the main figures and supporting information.</p
The obtained cost function values with respect to each run of different algorithms.</p
<p>Parameters of the different approaches algorithms depending on the computational budget.</p
<p>Illustration of Pareto fronts obtained by the complete enumeration with different values of .</p
<p>The numerical results of averaged PEP produced by all the three methods for each data-truncation ...
<p>The numerical value of the most ideal point (a), the least ideal point (b) and the mathematical e...
Number of fields on the Pareto front for each quality and random effect combination.</p
<p>Parameters derived from fitting of data to the Hill Equation with a Hill co-efficient of one.</p
In all subfigures red lines are contours of noise performance function, blue lines are contours of f...
The values of each parameter for each step of running proposed algorithm on the DAG graph of Fig 3.<...
<p>The tradeoff between energy cost and RMS tracking error shown as a Pareto front.</p
<p>Numerical values of the skin friction coefficient for different values of physical parameters.</...
<p>Numerical values of skin friction coefficients for different values of physical parameters.</p
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