Performance Comparison of Jumping Gene Adaptations of the Elitist Non‐dominated Sorting Genetic Algorithm

Industrial problems are complex in nature, and often have multiple performance criteria. The elitist nondominated sorting genetic algorithm (NSGA-II) has been used to optimize many process design and operation problems for two or more objectives. In order to improve the performance of this algorithm, the jumping-gene concept from natural genetics has been incorporated in NSGA-II. Several jumping-gene adaptations have been proposed and used to solve mathematical and application problems in different studies. In this chapter, four jumping-gene adaptations are selected and comprehensively evaluated on a number of two-objective unconstrained and constrained test functions. Three quality metrics, namely, generational distance, spread and inverse generational distance are employed to evaluate the distribution and convergence of the obtained Pareto-optimal solutions at the final generation and also at selected intermediate generations. Additionally, a search termination criterion based on the improvement in the Pareto-optimal front, has been described and used to check convergence of NGSA-II with the selected jumping gene adaptations.