Dynamic means that is changeable, adaptable, and flexible with respect to changesin requirements of the problem to solve. Here Dynamic GA (DyGA) is related tochanging population structure to create reliability and efficiency in solving a problem.Normally, the population of the standard GA is static, in other word there is nopossibility to change the chromosome length. This is considered as a problem withrespect to the speed of GA and the possibility to obtain a global optimal solution.In Dynamic GA (DyGA) the structure of the chromosomes is different (number ofgenes is variable) and population can change in size based on the problem requirements.Structure of the Dynamic Genetic AlgorithmDynamic GA is different from other conventional evolutionary algorithms specificallytraditional GA. The difference comes from its capacity to use chromosomeswith variable number of genes. The length of each chromosome is ended with asymbol to mark the completion of the chromosome length (Figure 3). Two chromosomes are selected randomly to reproduce in order to create new two new children(siblings). DyGA success to reach a global optimal solution depends on many factorsone of them is the probability of operators such as crossover . It should becarefully selected to avoid fast convergence to a local optimal solution and it can bedone by running many tests. The created children replace their parents, their fitnessvalues are computed again. There are two main conditions that must be satisfied forthe successful application of any new Dynamic structure of a chromosome: 1. Mutationoperator should be applied only on the genes while symbols should be avoided.2. Care should be taken in selecting the location of the crossover and mutation pointsaway from symbols. DyGA use has many advantages such as the reduction of thetime required to find an optimal solution. The cause of this improvement is due tothe new structure of the chromosome. The length is variable in terms of the numberof genes allocated to each chromosome. Moreover, the time spent in executingoperators for reproduction purposes and in computation (objective and fitness functions)is way less than that required by a normal GA with fixed length chromosome.Dynamic length chromosome guarantees diversity in the population , which can provideeasily the global optimal solution with minimum time requirement. DynamicGA can be hybridized in this case it is called Hybrid DyGA. In addition, Hill Climbingprocess is used to investigate adjacent points in the search space, and to help indriving the solution to the direction of having the best fitness value. The Hill Climbingprocess inspect cluster centers in each chromosome in details and frequentlymodifies the chromosome to increase its fitness value. Hill Climbing is an exploitationtechnique capable of finding local minima 45. For a better understanding ofthe new dynamic method, the following is the pseudo-code of HyDyGA. It is noticeablethat HyDyGA resembles HyGA, however the reproduction process is differentwith respect to the selection points for exchanging genes between chromosomes andwith respect to mutation .