Sub-Dividing Genetic Method for Optimization Problems
This addresses computational efficiency in optimization problems, but it appears incremental as it builds on existing genetic methods with sub-division techniques.
The paper tackles the high computational cost of finding global optima in continuous functions by proposing the Sub-Dividing Genetic Method (SGM), which reduces computation compared to other methods like Grefensstette, Random Value, and PNG, as demonstrated on the De Jong function.
Nowadays, optimization problem have more application in all major but they have problem in computation. Computation global point in continuous functions have high calculation and this became clearer in large space .In this paper, we proposed Sub- Dividing Genetic Method(SGM) that have less computation than other method for achieving global points . This method userotation mutation and crossover based sub-division method that sub diving method is used for minimize search space and rotation mutation with crossover is used for finding global optimal points. In experimental, SGM algorithm is implemented on De Jong function. The numerical examples show that SGM is performed more optimal than other methods such as Grefensstette, Random Value, and PNG.