Improving local search in genetic algorithms for numerical global optimization using modified GRID-point search technique

This paper presents a hybrid system for numerical global optimization problems based on Genetic Algorithms (GAs) and modified GRID-point search. Experimental results indicate that the hybrid system outperforms the classical GAs as the modified GRID can (i) speed up the search, (ii) further improve the fine tuning capabilities of GAs, and (iii) overcome the premature termination. The hybrid system not only improves the searching capabilities of classical GAs but it also preserves the randomization of the searching space. In addition, the effectiveness of the genetic operators is addressed in this paper.