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Nature-inspired optimization algorithms have received more and more attention from researchers due to their several advantages. Genetic algorithm (GA) is one of such bio-inspired optimization techniques, which has mainly three operators, namely selection, crossover, and mutation. Several attempts had been made to make these operators of a GA more efficient in terms of performance and convergence rates. One of the critical stages in the genetic algorithm is the crossover process. The crossover operator is believed to be the main search operator in the working of a genetic algorithm (GA) as an optimization tool.A number of crossover operators exist in the GA literature; however, the search power to achieve both of the above aspects differs from one crossover to another. In genetic algorithm (GA), mutation is one of the most important operators responsible for maintaining diversity in the population. The objective of the present study is to introduce a newly designed crossover operator called Logistic Crossover which uses Logistic Distribution. In a genetic algorithm (GA), the mutation is one of the most important operators responsible for maintaining diversity in the population. For a real-coded genetic algorithm (RCGA), this mutation operator is applied variable-wise. In this study, a new positional exponential mutation operator has been proposed for an RCGA. The locations of mutated solutions are made biased to the said information of the problem with the higher probability value. Positional exponential mutation operator, a novel, simple, and efficient real-coded genetic algorithm (RCGA) is proposed and then employed to solve complex function optimization problems. For a real-coded genetic algorithm (RCGA), this mutation operator is applied variable-wise.
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