Genetic algorithm (GA) is used to solve a variety of optimization problems. Mutation operator also is responsible in GA for maintaining a desired level of diversity in the population. Here, a directional mutation operator is proposed for real-coded genetic algorithm (RGA) along with a directional crossover (DX) operator to improve its performance. These evolutionary operators use directional information to guide the search process in the most promising area of the variable space. The performance of an RGA with the proposed mutation operator and directional crossover (DX) is tested on six benchmark optimization problems of different complexities, and the results are compared to that of the RGAs with five other mutation schemes. The proposed IRGA is found to outperform other RGAs in terms of accuracy in the solutions, convergence rate, and computational time, which is established firmly through statistical analysis. Furthermore, the performance of the proposed IRGA is compared to that of a few recently proposed optimization algorithms. The proposed IRGA is seen to yield the superior results compared to that of the said techniques. It is also applied to solve five constrained engineering optimization problems, where again, it has proved its supremacy. The proposed mutation scheme using directional information leads to an efficient search, and consequently, a superior performance is obtained.

遗传算法（GA）用于解决各种优化问题。变异算子在GA中也负责维持种群中所需的多样性水平。在此，针对定向编码遗传算法（RGA）提出了一种定向变异算子以及一种定向交叉（DX）算子，以提高其性能。这些进化算子使用方向信息来指导可变空间最有希望的区域中的搜索过程。在六个复杂度不同的基准优化问题上测试了带有拟议的突变算子和方向交叉（DX）的RGA的性能，并将结果与​​其他五个突变方案的RGA进行了比较。我们发现，在解决方案的准确性，收敛速度，和计算时间，这是通过统计分析确定的。此外，将提出的IRGA的性能与最近提出的一些优化算法的性能进行了比较。与所述技术相比，所提出的IRGA被认为产生了优异的结果。它也可用于解决五个约束工程优化问题，再次证明了它的优越性。提出的使用方向信息的突变方案导致有效的搜索，因此，获得了优异的性能。它也可用于解决五个约束工程优化问题，再次证明了它的优越性。提出的使用方向信息的突变方案导致有效的搜索，因此，获得了优异的性能。它也可用于解决五个约束工程优化问题，再次证明了它的优越性。提出的使用方向信息的突变方案导致有效的搜索，因此，获得了优异的性能。