Search Results

Combinatorial optimization problems are often NP-hard and too complex to be solved within a reasonable time frame by exact methods. Heuristic methods which do not offer a convergence guarantee could obtain some satisfactory resolution for combinatorial optimization problems. However, it is not only very time consuming for Central Processing Units (CPU) but also very difficult…

Combinatorial optimization is an area of great importance since many of the real-world problems have discrete parameters which are part of the objective function to be optimized. Development of combinatorial optimization algorithms is guided by the empirical study of the candidate ideas and their performance over a wide range of settings or scenarios to infer…

Combinatorial software testing is regarded as a crucial part when it comes to the software development life cycle. However, it would be impractical to exhaustive test highly configurable software due to limited time as well as resources. Moreover, a combinatorial testing strategy would be to employ input-output-based relations (IORs) due to its benefits versus other…

The objective of the metaheuristics, together with obtaining quality results in reasonable time, is to be able to control the exploration and exploitation balance within the iterative processes of these methodologies. Large combinatorial problems present ample search space, so Metaheuristics must efficiently explore this space; and exploits looking in the vicinity of good solutions previously…

This paper proposes a computationally intelligent algorithm for extracting relevant features from a training set. An optimal subset of features is extracted from training examples of network intrusion datasets. The Support Vector Machine (SVM) algorithm is used as the cost function within the thermal equilibrium loop of the Simulated Annealing (SA) algorithm. The proposed fusion…