Genetic algorithm has been used to optimize the MIS/IL solar cell parameters by changing the device physical parameters namely, doping concentration NA, oxide thickness dox, metal work function external back bias on the inversion grid V, mobile charge density Nm and fixed oxide charge density Nf [12]. Binary encoding scheme is used in this algorithm to encode the MIS/IL solar cell parameter [13-14]. The chromosome contains all parameters as shown in Fig.3. Each gene parameter encoded as 4-bit to include sixteen quantized values, as shown in Fig.4, and eight populations are selected to be greater than the number of genes per chromosome. Elitism is used to save the best solutions to improve the performance of the genetic algorithm [14].

The algorithm is started with a set of solutions (represented by chromosomes) called population. Solutions from one population are used to form a new population. This is motivated by a hope, that the new population will be better than the old one. Solutions which are selected to form new solutions (offspring) are selected according to their fitness. The more suitable they are the more chances they have to be reproduced. This is repeated until some conditions (for example number of populations or improvement of the best solution) is satisfied. The genetic algorithm proceed as follows, [13]

(1) Create a population of random individuals which represents a possible solution to the problem at hand.

(2) Evaluate each individual fitness i. e. its ability to solve the specified problems.

(3) Select individual population members to be parents.

(4) Produce children by recombining parent’s material via crossover and mutation and add them to the population.

(5) Evaluate the children fitness.

(6) Repeat steps (3-5) until a solution with the desired fitness goal is obtained [15-16]. The genetic algorithm flow chart of the optimization problem is shown in Fig.5.