Genetic algorithm (GA) is a heuristic search technique that draws inspiration from principles and mechanisms of natural selection. Conventionally, parents selection takes place at every generation and offspring are reproduced through genetic operators like crossover and mutation. The process reiterates until some termination conditions are met. Until recently, little attention has been paid on the enduring relationship between parent solutions. In this paper, we take on and further extend the idea of monogamous genetic algorithm to solving real-coded numerical optimization problems. In this GA model, each monogamous pair of parents yields two offspring, and only the best two offspring survive into the next generation. Occasional infidelity generates variety and promotes diversity in the population. Simulation results over the IEEE-CEC’13 (IEEE Congress on Evolutionary Computation 2013) contest for real parameter single objective optimization with 28 benchmark functions demonstrate the effectiveness of the proposed approach.