**4.2 The techniques of GA and image fusion**

### **4.2.1 Genetic Algorithm**

As stated earlier, GA is a non-linear optimization technique that seeks the optimum solution of a function via a non-exhaustive search among randomly generated solutions. GAs use multiple search points instead of searching one point at a time and attempt to find global, near-optimal solutions without getting stuck at local optima. Because of these significant advantages, GAs reduce the search time and space. However, there are disadvantages of using GAs as well: they are not generally suitable for real-time applications since the time to converge to an optimal solution cannot be predicted. The convergence time depends on the population size, and the GA crossover and mutation operators. In this fusion process, a continuous genetic algorithm has been selected.
