Given the computational complexity of the instance addressed, similar or higher versions of it could be solved using another class of algorithms such as Traveling Salesman, Dendrite, the Ant Colony Algorithm, the Chinese Postman Problem, Hamiltonian Cycle, Longest Path Problem, Optimization, Plateau's Problem, Road Coloring Problem, Constants and others.
The improvement tends to raise as the complexity of the analyzed instance increases.
It integrates the compositional complexity of motif instances into the function.
It is well known that in supervised learning, active learning could effectively decrease the complexity of training instances without obvious loss of the classification performance.
In this section, we describe five measures that examine the shape of the class boundary to estimate the complexity of separating instances of different classes.
Also, in order to determine computational complexity of the algorithm, 15 instance problems are solved ranging from 10 to 100 trains which the produced results are illustrated in Figure 1.
As such, until the technology surrounding computational resources improves, other methods must be found to exploit the power of the correlation network by reducing the size and complexity of the problem (for instance, by network filtering, which looks to graph theoretic properties to reduce edge and node count [ 25]).
The paper fills this gap, by studying the computational complexity of MMUCA instances under structural and qualitative restrictions, which characterize interactions among bidders and types of bids involved in the various transformations, respectively.
The complexity of instances SMD7 and SMD8 emerges from the fact that there exist multimodalities in the upper level objective function landscape.
