Sentence examples for minimum fitness function from inspiring English sources

Result will be positions of bacteria with minimum fitness function output.

Assuming an area lower than twice of the minimum fitness function (inside the 0.3 contour area in Fig. 7) for the parameter space, the depth and volumetric change have ranges of 7.8 14.5 km and −6.0 −24.8 × 106 m3, respectively.

On each iteration, these two matrices are updated according to Eqs. (3) and (4) in such a way that a minimum fitness function cost (i.e., Eq. 1) is achieved while producing the optimal clustering structure.

An intergeneration-projection genetic-algorithm (IP-GA) is introduced as the inverse procedure to speed up the process of finding the desired global minimum of the fitness function of error that leads to the parameters to be identified.

With these parameters adjusted, the BSA is used to search for the global minimum of the fitness function described in Section 3.1.1 and, therefore, discover the model of the attacked device.

The error is calculated utilizing the minimum of the fitness function of these outcomes given across domains of various measures ((h=k= 0.1, 0.2).) The comparison is imposed by well-known techniques, such as the Genetic Algorithm (GA), LMI, and PSOA.

PSO belongs to the class of swarm intelligence algorithms, which use a set of particles that move with a certain velocity to search the minimum of a fitness function.

The functional parameters required for GA have been determined experimentally by finding out the minimum cost of the fitness function (Eq. 1) and are presented in Table 3 in Sect.

ES, realGA, and DE are evolutionary algorithms, which use mutation, crossover, and selection operators to search the minimum of a certain fitness function.

The impact of the switching controller in the attack performance can also be verified through the analysis of the global minimum values obtained for the fitness function (3).

In general, an evolutionary search seeks out a global minimum of certain fitness functions based on the heuristic assumption that best solutions will be found in the regions of the parameter space by using the genetic operations of selection, crossover, and mutation.