Abstract Like elitism, parent inheritance plays an important role to decide the quality of offspring and it is believed that the parents with high intelligence quotient (IQ) like to produce children with high IQ. Inspiring this concept, the improved pool of an initial random population involving the best set of chromosomes are incorporated in the framework of multi-objective optimization genetic algorithm. The effects of parent inheritance in the elitist non-dominated sorting genetic algorithm (called, i-NSGA-II) on the speed of convergence to the global Pareto-optimal front is compared with the binary coded NSGA-II using different benchmark multi-objective optimization problems. The parent inheritance is also incorporated in several jumping gene (JG) adapted NSGA-II algorithms. The efficacy of inheritance in NSGA-II and its several JG adaptations is tested by quantifying several indicators, namely, generational distance, spacing and hyper-volume ratio using different benchmark multi-objective optimization problems from the literature. The inclusion of the inheritance operator improves the speed of convergence to global Pareto-optimal front significantly with a minimum number of generations over existing NSGA-II and several JG adapted NSGA-II algorithms. The effectiveness of the proposed operator is further established by solving real-life robust multi-objective optimization problems involving the drilling of oil-well and synthesis of sal oil biodiesel.