 = p a w (8.9) subject to Pfsa where a is some acceptable level of probability of false alarm. To implement the above optimization task, we implement two heuristic algorithms. Namely, the genetic 5G Wireless Communication System in Healthcare Informatics algorithm with multiparent crossover (GA-MPC) and the cross entropy (CE) opti- mization methods, which are explained next. 8.5.1 GENETIC ALGORITHM WITH MULTI PARENT CROSSOVER The genetic algorithm (GA) is a well-known heuristic optimization technique which has been applied to numerous real-world optimization problems. In the GA, there are two main operations involved which are crossover and mutation. There are different techniques to perform these operations. In this context, various multi-parent cross- over operations are developed such as simplex crossover, parent centric crossover, tri- angular crossover, and unimodal distribution crossover. Elsayed et al. [25] proposed a multi-parent crossover operation based on the mutation strategy of differential evo- lution. The GA-MPC has shown superior performance in contrast to classical single parent crossover based GA. Thus, the GA-MPC has been successfully applied in various well known problems such as static and dynamic dispatching, transmis- sion networks, catalyst blends, benchmark functions planning, transmission pricing, optimal control, and antenna design. 8.5.1.1 GA-MPC Crossover Operation The process of crossover in the GA-MPC involves five major steps that are as follows: 1. A selection pool is prepared by selecting various chromosomes using the defined selection operation. 2. In order to avoid duplication, the selected parents are checked and the duplicated one is replaced by a random chromosome from the selection pool. 3. The selected parents are sorted w.r.t. to their level of best fit. 4. A tuning parameter to control the crossover operation is initialized randomly. 5. Finally, the three offspring are generated according to the following equations: 8.5.2 CROSS ENTROPY METHOD The cross-entropy (CE) metho