**4. Results and discussions**

Therefore, according to (5) strategies with high success rates have higher probability to be

The control parameters are self-adapted in the following way. The mutation control parameter *F* is approximated by a normal distribution with mean value 0.5 and standard deviation 0.3, that is *N* (0.5, 0.3). The parameter *K* is a random number in the interval [0, 1] generated by a uniform distribution. The crossover rate control parameter *CR* used by the *m-th* strategy is also approximated by a normal distribution with mean value *CRm* and standard deviation 0.1, that is *N* (*CRm*, 0.1). The initial value of *CRm* is 0.5 for all strategies. The values of crossover rates that have successfully generated trial vectors in the previous LP generations are stored in a crossover rate memory for each strategy *CRmmemory* that is an array of size LP. At each gener‐ ation, the median value stored in memory for the *m-th* strategy *CRmmedian* is calculated and the *CR* values generated are given by a normal distribution with mean value *CRmmedian* and standard deviation 0.1. That way the crossover values are evolved at each generation to follow the successful values found. The authors in [17] suggest a value between 20 and 60 for the parameter LP. The sensitivity analysis performed in [17] for the LP parameter showed it had no significant impact on SaDE performance. More details about the SaDE algorithm can be

This section presents a brief literature review of applications of evolutionary algorithms and

Genetic algorithms (GAs) are among the widely used optimization techniques for address‐ ing design problems in wireless communications. In [26] a Smith prediction filter is proposed for power control design of direct-sequence code-division multiple-access cellular mobile radio systems. A fixed-order robust H∞ loop filter is developed using a genetic algorithm to minimize the worst-case variance of the received SINR from the minimax perspective. The authors in [27] present an antenna selection method for multiple-input multiple-output wireless systems based on a GA that seeks the best subset of antenna elements. The problem of receive antenna selection and symbol detection for multipleinput, multiple-output (MIMO) systems is solved in [28] by applying a genetic algorithm (GA) variant. The paper in [29] addresses the problem of joint transmit/receive antenna selection for MIMO systems using a real-valued genetic algorithm (RVGA). The optimiza‐ tion objective is to improve the channel capacity of multiple-input/multiple-output (MIMO) systems. The study in [30] presents a pattern discovery algorithm for multi-streams mining in wireless sensor networks. This algorithm adapts genetic operators with Elitism Strat‐ egy.The paper in [31] studies joint multiuser linear precoding design in the forward link of fixed multibeam satellite systems. The authors use a generic optimization framework for linear precoding design to handle any objective functions of data rate with general linear and nonlinear power constraints. In [32] an energy-efficient genetic algorithm mechanism

applied at the current generation.

8 Contemporary Issues in Wireless Communications

found in [17].

**3. Related work**

their variants to wireless communications problems.

In this section we present numerical results from different optimization problems in wireless communications using different algorithms.
