A dynamic approach for parameter tuning in genetic algorithm using crossover and mutation ratios

Genetic algorithm uses the natural selection process for any search process. It is an optimization where integration among different vital parameters like crossover and mutation plays a major role. The have impact on by their probabilities. In this paper we would review strategies used of ratios suggest dynamic approach modifying during runtime. We start with ratio 0% 100% slowly increases decreases (MICD). final will be at end also do reverse considering to maximum minimum decrease increase (MDCI). compare proposed method two pre-existing parameter tuning methods found that incrementing decrementing value was more effective when size population large.