The multi-objective optimization of industrial operations using genetic algorithm and its variants, often requires inordinately large amounts of computational (CPU) time. Any adaptation to speed up the solution procedure is, thus, desirable. An adaptation is developed in this study that is inspired from natural genetics. It is based on the concept of jumping genes (JG; transposons). The binary-coded elitist non-dominated sorting genetic algorithm (NSGA-II) is adapted, and the new code, NSGA-II-JG, is used to obtain solutions for the multi-objective optimization of an industrial fluidized-bed catalytic cracking unit (FCCU). This unit is associated with a complex model that is highly compute-intense. The CPU time required for this problem is found to reduce fivefold when NSGA-II-JG is used, as compared to when NSGA-II is used. Solutions of similar two-objective optimization problems for the FCCU are compared. NSGA-II-JG also gives improved convergence characteristics and spread of the optimal Pareto points for two simpler multi-objective optimization problems studied here. Indeed, in one problem, where several optimal Pareto fronts exist, the new code gives the correct, global optimal Pareto set, while the original code (binary-coded NSGA-II) converges to local Paretos. The JG operator is associated with some kind of macro-macro-mutation and introduces higher exploratory capabilities, counteracting the effect of elitism in NSGA-II. We, thus, have a better algorithm incorporating the advantages of elitism. This adaptation can prove to be of considerable value for solving other compute-intense problems in chemical engineering. (C) 2003 Elsevier Ltd. All rights reserved.