Sub-Dividing Genetic Method for Optimization Problems

TL;DR

This paper introduces the Sub-Dividing Genetic Method (SGM), a novel optimization technique that reduces computation by dividing the search space and applying rotation mutation and crossover, demonstrating superior performance on benchmark functions.

Contribution

The paper presents a new genetic algorithm variant that minimizes search space and improves global optimization efficiency compared to existing methods.

Findings

SGM outperforms Grefensstette, Random Value, and PNG methods.

SGM reduces computational effort in finding global optima.

Experimental results on De Jong function validate the effectiveness of SGM.

Abstract

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.